Choosing the Right LLM Inference Framework: A Practical Guide

Performance benchmarks, cost analysis, and decision framework for developers worldwide

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Here’s something nobody tells you about “open source” AI: the model weights might be free, but running them isn’t.

A developer in San Francisco downloads LLaMA-2 70B. A developer in Bangalore downloads the same model. They both have “open access.” But the San Francisco developer spins up an A100 GPU on AWS for $2.50/hour and starts building. The Bangalore developer looks at their budget, does the math on ₹2 lakhs per month for cloud GPUs, and realizes that “open” doesn’t mean “accessible.”

This is where LLM inference frameworks come in. They’re not just about making models run faster—though they do that. They’re about making the difference between an idea that costs $50,000 a month to run and one that runs on your laptop. Between building something in Singapore that requires data to stay in-region and something that phones home to Virginia with every request. Between a prototype that takes two hours to set up and one that takes two weeks.

The framework you choose determines whether you can actually build what you’re imagining, or whether you’re locked out by hardware requirements you can’t meet. So let’s talk about how to choose one.

What This Guide Covers (And What It Doesn’t)

This guide focuses exclusively on inference and serving constraints for deploying LLMs in production or development environments. It compares frameworks based on performance, cost, setup complexity, and real-world deployment scenarios.

What this guide does NOT cover:

• Model quality, alignment, or training techniques
• Fine-tuning or model customization approaches
• Prompt engineering or application-level optimization
• Specific model recommendations (LLaMA vs GPT vs others)

If you’re looking for help choosing which model to use, this isn’t the right guide. This is about deploying whatever model you’ve already chosen.

What You Need to Know

Quick Answer: Choose vLLM if you’re deploying at production scale (100+ concurrent users) and need consistently low latency. Choose TensorRT-LLM if you’re on NVIDIA hardware and can invest 1-2 weeks in setup for maximum throughput efficiency. Choose Ollama if you’re prototyping and want something running in 10 minutes. Choose llama.cpp if you don’t have access to GPUs or need to deploy on edge devices.

The Real Question: This isn’t actually about which framework is “best.” It’s about which constraints you’re operating under. A bootstrapped startup in Pune and a funded company in Singapore are solving fundamentally different problems, even if they’re deploying the same model. The “best” framework is the one you can actually use.

Understanding LLM Inference Frameworks

What is an LLM Inference Framework?

An LLM inference framework is specialized software that handles everything involved in getting predictions out of a trained language model. Think of it as the engine that sits between your model weights and your users.

When someone asks your chatbot a question, the framework manages: loading the model into memory, batching requests from multiple users efficiently, managing the key-value cache that speeds up generation, scheduling GPU computation, handling the token-by-token generation process, and streaming responses back to users.

Without an inference framework, you’d need to write all of this yourself. With one, you get years of optimization work from teams at UC Berkeley, NVIDIA, Hugging Face, and others who’ve solved these problems at scale.

Why This Choice Actually Matters

The framework you choose determines three things that directly impact whether your project succeeds:

Cost. A framework that delivers 120 requests per second versus 180 requests per second means the difference between renting 5 GPUs or 3 GPUs. At $2,500 per GPU per month, that’s $5,000 monthly—$60,000 annually. For a startup, that’s hiring another engineer. For a bootstrapped founder, that’s the difference between profitable and broke.

Time. Ollama takes an hour to set up. TensorRT-LLM can take two weeks of expert time. If you’re a solo developer, two weeks is an eternity. If you’re a funded team with ML engineers, it might be worth it for the performance gains. Time-to-market often matters more than theoretical optimization.

What you can build. Some frameworks need GPUs. Others run on CPUs. Some work on any hardware; others are locked to NVIDIA. If you’re in a region where A100s cost 3x what they cost in Virginia, or if your data can’t leave Singapore, these constraints determine what’s possible before you write a single line of code.

The Six Frameworks You Should Know

Let’s cut through the noise. There are dozens of inference frameworks, but six dominate the landscape in 2025. Each makes different trade-offs, and understanding those trade-offs is how you choose.

vLLM: The Production Workhorse

What it is: Open-source inference engine from UC Berkeley’s Sky Computing Lab, now a PyTorch Foundation project. Built for high-throughput serving with two key innovations—PagedAttention and continuous batching.

Performance: In published benchmarks and production deployments, vLLM typically delivers throughput in the 120-160 requests per second range with 50-80ms time to first token. What makes vLLM special isn’t raw speed—TensorRT-LLM can achieve higher peak throughput—but how well it handles concurrency. It maintains consistently low latency even as you scale from 10 users to 100 users.

Setup complexity: 1-2 days for someone comfortable with Python and CUDA. The documentation is solid, the community is active, and it plays nicely with Hugging Face models out of the box.

Best for: Production APIs serving multiple concurrent users. Interactive applications where time-to-first-token matters. Teams that want flexibility without weeks of setup time.

Real-world example: A Bangalore-based SaaS company with Series A funding uses vLLM to power their customer support chatbot. They handle 50-100 concurrent users during business hours, running on 2x A100 GPUs in AWS Mumbai region. Monthly cost: ₹4 lakhs ($4,800). They chose vLLM over TensorRT-LLM because their ML engineer could get it production-ready in a week versus a month.

TensorRT-LLM: Maximum Performance, Maximum Complexity

What it is: NVIDIA’s specialized inference library built on TensorRT. Not a general-purpose tool—this is specifically engineered to extract every possible bit of performance from NVIDIA GPUs through CUDA graph optimizations, fused kernels, and Tensor Core acceleration.

Performance: When properly configured on supported NVIDIA hardware, TensorRT-LLM can achieve throughput in the 180-220 requests per second range with 35-50ms time to first token at lower concurrency levels. Published benchmarks from BentoML show it delivering up to 700 tokens per second when serving 100 concurrent users with LLaMA-3 70B quantized to 4-bit. However, under certain batching configurations or high concurrency patterns, time-to-first-token can degrade significantly—in some deployments, TTFT can exceed several seconds when not properly tuned.

Setup complexity: 1-2 weeks of expert time. You need to convert model checkpoints, build TensorRT engines, configure Triton Inference Server, and tune parameters. The documentation exists but assumes you know what you’re doing. For teams without dedicated ML infrastructure engineers, this can feel like climbing a mountain.

Best for: Organizations deep in the NVIDIA ecosystem willing to invest setup time for maximum efficiency. Enterprise deployments where squeezing 20-30% more throughput from the same hardware justifies weeks of engineering work.

Real-world example: A Singapore fintech company processing legal documents uses TensorRT-LLM on H100 GPUs. They handle 200+ concurrent users and need data to stay in the Singapore region for compliance. The two-week setup time was worth it because the performance gains let them use 3 GPUs instead of 5, saving S$8,000 monthly.

Ollama: Developer-Friendly, Production-Limited

What it is: Built on llama.cpp but wrapped in a polished, developer-friendly interface. Think of it as the Docker of LLM inference—you can get a model running with a single command.

Performance: In typical development scenarios, Ollama handles 1-3 requests per second in concurrent situations. This isn’t a production serving framework—it’s optimized for single-user development environments. But for that use case, it’s exceptionally good.

Setup complexity: 1-2 hours. Install Ollama, run ‘ollama pull llama2’, and you’re running a 7B model on your laptop. It handles model downloads, quantization, and serving automatically.

Best for: Rapid prototyping. Learning how LLMs work without cloud bills. Individual developers building tools for themselves. Any situation where ease of use matters more than serving many concurrent users.

Real-world example: A solo developer in Austin building a personal research assistant uses Ollama on a MacBook Pro. Zero cloud costs. Zero setup complexity. When they’re ready to scale, they’ll migrate to vLLM, but for prototyping, Ollama gets them building immediately instead of fighting infrastructure.

llama.cpp: The CPU Enabler

What it is: Pure C/C++ implementation with no external dependencies, designed to run LLMs on consumer hardware. This is the framework that makes “I don’t have a GPU” stop being a blocker.

Performance: CPU-bound, meaning it depends heavily on your hardware. But with aggressive quantization (down to 2-bit), you can run a 7B model at usable speeds on a decent CPU. Not fast enough for 100 concurrent users, but fast enough for real applications serving moderate traffic.

Setup complexity: Hours to days, depending on your comfort with C++ compilation and quantization. More involved than Ollama, less involved than TensorRT-LLM.

Best for: Edge deployment. Resource-constrained environments. Any scenario where GPU access is impossible or prohibitively expensive. Developers who need maximum control over every optimization.

Real-world example: An ed-tech startup in Pune runs llama.cpp on CPU servers, serving 50,000 queries daily for their AI tutor product. Monthly infrastructure cost: ₹15,000 ($180). They tried GPU options first, but ₹2 lakhs per month wasn’t sustainable at their revenue. CPU inference is slower, but their users don’t notice the difference between 200ms and 800ms response times.

Hugging Face TGI: Ecosystem Integration

What it is: Text Generation Inference from Hugging Face, built for teams already using the HF ecosystem. It’s not the fastest framework, but the integration with Hugging Face’s model hub and tooling makes it valuable for certain workflows.

Performance: In practice, TGI delivers throughput in the 100-140 requests per second range with 60-90ms time to first token. Competitive but not leading-edge.

Best for: Teams already standardized on Hugging Face tooling. Organizations that value comprehensive documentation and established patterns over cutting-edge performance.

SGLang: Structured Generation Specialist

What it is: Framework built around structured generation with a dedicated scripting language for chaining operations. RadixAttention enables efficient cache reuse for sequences with similar prefixes.

Performance: SGLang shows remarkably stable per-token latency (4-21ms) across different load patterns. Not the highest throughput, but notably consistent.

Best for: Multi-step reasoning tasks, agentic applications, integration with vision and retrieval models. Teams building complex LLM workflows beyond simple chat.

Understanding Performance Metrics

When people talk about inference performance, they’re usually talking about three numbers. Understanding what they actually mean helps you choose the right framework.

Performance Benchmark Caveat

Performance metrics vary significantly based on:

• Model size and quantization level
• Prompt length and output length
• Batch size and concurrency patterns
• GPU memory configuration and hardware specs
• Framework version and configuration tuning

The figures cited in this guide represent observed ranges from published benchmarks (BentoML, SqueezeBits, Clarifai) and real-world deployment reports from 2024-2025. They are not guarantees and should be validated against your specific workload before making infrastructure decisions.

Time to First Token (TTFT)

This is the delay between when a user sends a request and when they see the first word of the response. For interactive applications—chatbots, coding assistants, anything with humans waiting—this is what determines whether your app feels fast or sluggish.

Think about asking ChatGPT a question. That pause before the first word appears? That’s TTFT. Below 100ms feels instant. Above 500ms starts feeling slow. Above 1 second, users notice and get frustrated.

In published benchmarks, vLLM excels here, maintaining 50-80ms TTFT even with 100 concurrent users. TensorRT-LLM achieves faster times at low concurrency (35-50ms) but can degrade under certain high-load configurations.

Throughput (Requests per Second)

This measures how many requests your system can handle simultaneously. Higher throughput means you need fewer servers to handle the same traffic, which directly translates to lower costs.

In optimized deployments, TensorRT-LLM can achieve 180-220 req/sec, vLLM typically delivers 120-160 req/sec, and TGI manages 100-140 req/sec. At scale, these differences matter. Going from 120 to 180 req/sec means you can serve 50% more users on the same hardware.

But here’s the catch: throughput measured in isolation can be misleading. What matters is sustained throughput while maintaining acceptable latency. A framework that delivers 200 req/sec but with 2-second TTFT isn’t actually useful for interactive applications.

Tokens Per Second (Decoding Speed)

After that first token appears, this measures how fast the model generates the rest of the response. This is what makes responses feel fluid when they’re streaming.

Most modern frameworks deliver 40-60 tokens per second on decent hardware. The differences here are smaller than TTFT or throughput, and honestly, most users don’t notice the difference between 45 and 55 tokens per second when watching a response stream in.

The Real Cost Analysis

Let’s talk about what it actually costs to run these frameworks. The numbers vary wildly depending on where you are and what you’re building.

Pricing Disclaimer

Cloud provider pricing fluctuates based on region, commitment level, and market conditions. The figures below reflect typical 2024-2025 ranges from AWS, GCP, and Azure. Always check current pricing for your specific region and usage pattern before making budget decisions.

Hardware Costs

Purchasing an A100 GPU:

• United States: $10,000-$15,000
• Singapore: S$13,500-S$20,000
• India: ₹8-12 lakhs

Cloud GPU rental (monthly):

• AWS/GCP US regions: $2,000-3,000/month per A100
• AWS Singapore: S$2,700-4,000/month per A100
• AWS Mumbai: ₹1.5-2.5 lakhs/month per A100

That’s just the GPU. You also need CPU, memory, storage, and bandwidth. A realistic production setup costs 20-30% more than just the GPU rental.

The Setup Cost Nobody Talks About

Engineering time is real money, even if it doesn’t show up on your AWS bill.

Ollama: 1-2 hours of developer time. At ₹5,000/hour for a senior developer in India, that’s ₹10,000. At $150/hour in the US, that’s $300. Basically free.

vLLM: 1-2 days of ML engineer time. In India, maybe ₹80,000. In the US, $2,400. In Singapore, S$1,600. Not trivial, but manageable.

TensorRT-LLM: 1-2 weeks of expert time. In India, ₹4-5 lakhs. In the US, $12,000-15,000. In Singapore, S$8,000-10,000. Now you’re talking about real money.

For a bootstrapped startup, that TensorRT-LLM setup cost might be more than their entire monthly runway. For a funded company with dedicated ML infrastructure engineers, it’s a rounding error worth paying for the performance gains.

Regional Considerations

The framework choice looks different depending on where you’re building. Not because the technology is different, but because the constraints are different.

For Developers in India

Primary challenge: Limited GPU access and import costs that make hardware 3x more expensive than in the US.

The llama.cpp advantage: When cloud GPUs cost ₹2 lakhs per month and that’s your entire team’s salary budget, CPU-only inference stops being a compromise and starts being the only viable path. Advanced quantization techniques in llama.cpp can compress models down to 2-4 bits, making a 7B model run acceptably on a ₹15,000/month CPU server.

Real scenario: You’re building a SaaS product for Indian SMEs. Your customers won’t pay enterprise prices, so your margins are tight. Spending ₹2 lakhs monthly on infrastructure when your MRR is ₹8 lakhs doesn’t work. But ₹15,000 on CPU servers? That’s sustainable. You’re not trying to serve Google-scale traffic anyway—you’re trying to build a profitable business.

For Developers in Singapore and Southeast Asia

Primary challenge: Data sovereignty requirements and regional latency constraints.

The deployment reality: Financial services, healthcare, and government sectors in Singapore often require data to stay in-region. That means you can’t just use the cheapest cloud region—you need Singapore infrastructure. AWS Singapore costs about 10% more than US regions, but that’s the cost of compliance.

Framework choice: vLLM or TGI on AWS Singapore or Google Cloud Singapore. The emphasis is less on absolute cheapest and more on reliable, compliant, production-ready serving. Teams here often have the budget for proper GPU infrastructure but need frameworks with enterprise support and proven reliability.

For Developers in the United States

Primary challenge: Competitive pressure for maximum performance and scale.

The optimization game: US companies often compete on features and scale where milliseconds matter and serving 10,000 concurrent users is table stakes. The cost of cloud infrastructure is high, but the cost of being slow or unable to scale is higher. Losing users to a faster competitor hurts more than spending an extra $10,000 monthly on GPUs.

Framework choice: Funded startups tend toward vLLM for the balance of performance and deployment speed. Enterprises with dedicated ML infrastructure teams often invest in TensorRT-LLM for that last 20% of performance optimization. The two-week setup time is justified because the ongoing savings from better GPU utilization pays for itself.

Quick Decision Matrix

Use this table as a starting point for framework selection based on your primary constraint:

Your Primary Constraint	Recommended Framework	Why
No GPU access	llama.cpp	CPU-only inference with aggressive quantization
Prototyping / Learning	Ollama	Zero-config, runs on laptops
10-100 concurrent users	vLLM	Best balance of performance and setup complexity
100+ users, NVIDIA GPUs	TensorRT-LLM	Maximum throughput when properly configured
Hugging Face ecosystem	TGI	Seamless integration with HF tools
Agentic/multi-step workflows	SGLang	Structured generation and cache optimization
Tight budget, moderate traffic	llama.cpp	Lowest infrastructure cost
Data sovereignty requirements	vLLM or TGI	Regional deployment flexibility

How to Actually Choose

Stop looking for the “best” framework. Start asking which constraints matter most to your situation.

Question 1: What’s Your Budget Reality?

Can’t afford GPUs at all: llama.cpp is your path. It’s not a compromise; it’s how you build something rather than nothing. Many successful products run on CPU inference because their users care about reliability and features, not sub-100ms response times.

Can afford 1-2 GPUs: vLLM or TGI. Both will get you production-ready inference serving reasonable traffic. vLLM probably has the edge on performance; TGI has the edge on ecosystem integration if you’re already using Hugging Face.

Can afford a GPU cluster: Now TensorRT-LLM becomes interesting. When you’re running 5+ GPUs, that 20-30% efficiency gain from TensorRT means you might only need 4 GPUs instead of 5. The setup complexity is still painful, but the ongoing savings justify it.

Question 2: How Much Time Do You Have?

Need something running today: Ollama. Install it, pull a model, start building. You’ll migrate to something else later when you need production scale, but Ollama gets you from zero to functional in an afternoon.

Have a week: vLLM or TGI. Both are production-capable and well-documented enough that a competent engineer can get them running in a few days.

Have dedicated ML infrastructure engineers: TensorRT-LLM becomes viable. The complexity only makes sense when you have people whose job is dealing with complexity.

Question 3: What Scale Are You Actually Targeting?

Personal project or internal tool (1-10 users): Ollama or llama.cpp. The overhead of vLLM’s production serving capabilities doesn’t make sense when you have 3 users.

Small SaaS (10-100 concurrent users): vLLM or TGI. You’re in the sweet spot where their optimizations actually matter but you don’t need absolute maximum performance.

Enterprise scale (100+ concurrent users): vLLM or TensorRT-LLM depending on whether you optimize for deployment speed or runtime efficiency. At this scale, the performance differences translate to actual money.

Question 4: What’s Your Hardware Situation?

NVIDIA GPUs available: All options are on the table. If it’s specifically A100/H100 hardware and you have time, TensorRT-LLM will give you the best performance when properly configured.

AMD GPUs or non-NVIDIA hardware: vLLM has broader hardware support. TensorRT-LLM is NVIDIA-only.

CPU only: llama.cpp is your only real option. But it’s a good option—don’t treat it as second-class.

Real-World Deployment Scenarios

Let’s look at how actual teams made these choices.

Scenario 1: Bootstrapped Startup in Bangalore

Company: Ed-tech platform, 5 person team, 50,000 daily users

Budget constraint: ₹8 lakhs monthly revenue, can’t spend ₹2 lakhs on infrastructure

Technical requirement: AI-powered personalized learning recommendations

Choice: llama.cpp on 32-core CPU servers

Outcome: ₹15,000/month infrastructure cost. Response times are 400-800ms, which their users don’t complain about because the recommendations are actually useful. The business is profitable, which wouldn’t be true with GPU costs.

Scenario 2: Series A SaaS Company in Singapore

Company: Financial services platform, 40 employees, serving banks and fintech companies

Regulatory constraint: Data must stay in Singapore region for compliance

Technical requirement: Process 10M financial documents monthly, 200+ concurrent users during business hours

Choice: TensorRT-LLM on 3x H100 GPUs in AWS Singapore region

Outcome: S$12,000/month infrastructure cost. The two-week setup time was painful, but the performance optimization meant they could handle their load on 3 GPUs instead of the 5 GPUs vLLM would have required. Monthly savings of S$8,000 justified the initial investment.

Scenario 3: AI Startup in San Francisco

Company: Developer tools company, 25 employees, $8M Series A

Market constraint: Competing with well-funded incumbents on performance

Technical requirement: Code completion with sub-100ms latency, 500+ concurrent developers

Choice: vLLM on 8x A100 GPUs

Outcome: $20,000/month infrastructure cost. They prioritized getting to market fast over squeezing out maximum performance. vLLM gave them production-quality serving in one week versus the month TensorRT-LLM would have taken. At their stage, speed to market mattered more than 20% better GPU efficiency.

The Uncomfortable Truth About Framework Choice

Here’s what nobody wants to say: for most developers, the framework choice is constrained by things that have nothing to do with the technology.

A developer in San Francisco and a developer in Bangalore might both download the same LLaMA-2 weights. They both have “open access” to the model. But they don’t have the same access to the infrastructure needed to run it at scale. The San Francisco developer can spin up A100 GPUs without thinking about it. The Bangalore developer does the math and realizes it would consume their entire salary budget.

This is why llama.cpp matters so much. Not because it’s the fastest or the most elegant solution, but because it’s the solution that works when GPUs aren’t an option. It’s the difference between building something and building nothing.

We talk about “democratizing AI” by releasing model weights. But if running those models costs $5,000 per month and your monthly income is $1,000, those weights aren’t democratized—they’re just decorative. The framework you can actually use determines whether you can build at all.

This isn’t a technical problem. It’s a structural one. And it’s why framework comparisons that only look at benchmarks miss the point. The “best” framework isn’t the one with the highest throughput. It’s the one that lets you build what you’re trying to build with the constraints you actually face.

Practical Recommendations

Based on everything we’ve covered, here’s how I’d think about the choice:

Start with Ollama for Prototyping

Unless you have unusual constraints, begin with Ollama. Get your idea working, validate that it’s useful, prove to yourself that LLM inference solves your problem. You’ll learn what performance characteristics actually matter to your users.

Don’t optimize prematurely. Don’t spend two weeks setting up TensorRT-LLM before you know if anyone wants what you’re building.

Graduate to vLLM for Production

When you have actual users and actual scale requirements, vLLM is probably your best bet. It’s the sweet spot between performance and deployment complexity. You can get it running in a few days, it handles production loads well, and the community is active if you run into issues.

vLLM’s superpower isn’t being the absolute fastest—it’s being fast enough while remaining deployable by teams without dedicated ML infrastructure engineers.

Consider TensorRT-LLM When Scale Justifies Complexity

If you’re running 5+ GPUs and burning $15,000+ monthly on infrastructure, now the two-week setup time for TensorRT-LLM starts making sense. A 25% performance improvement means you might only need 4 GPUs instead of 5, saving $3,000 monthly. That pays for the setup time in a few months.

But be honest about whether you’re at that scale. Most projects aren’t.

Don’t Dismiss llama.cpp

If your budget is tight or you need edge deployment, llama.cpp isn’t a fallback option—it’s the primary option. Many successful products run on CPU inference. Your users care about whether the product works, not whether it uses GPUs.

A working product on CPU infrastructure beats a hypothetical perfect product that you can’t afford to build.

Frequently Asked Questions

Which LLM inference framework should I choose?

It depends on your constraints. Choose vLLM for production scale (100+ concurrent users) with balanced setup complexity. Choose TensorRT-LLM if you’re on NVIDIA hardware and can invest 1-2 weeks for maximum performance. Choose Ollama for rapid prototyping and getting started quickly. Choose llama.cpp if you don’t have GPU access or need edge deployment.

Can I run LLM inference without a GPU?

Yes. llama.cpp enables CPU-only LLM inference with advanced quantization techniques that reduce memory requirements by up to 75%. While slower than GPU inference, it’s fast enough for many real-world applications, especially those serving moderate traffic rather than thousands of concurrent users. Many successful products run entirely on CPU infrastructure.

How much does LLM inference actually cost?

Cloud GPU rental varies by region: $2,000-3,000/month per A100 in the US, S$2,700-4,000/month in Singapore, ₹1.5-2.5 lakhs/month in India. CPU-only deployment with llama.cpp can cost as little as ₹10-15K/month ($120-180) for moderate workloads. The total cost includes setup time: Ollama takes hours, vLLM takes 1-2 days, TensorRT-LLM takes 1-2 weeks of expert engineering time.

Is vLLM better than TensorRT-LLM?

They optimize for different things. vLLM prioritizes ease of deployment and consistent low latency across varying loads. TensorRT-LLM prioritizes maximum throughput on NVIDIA hardware but requires significantly more setup effort. vLLM is better for teams that need production-ready serving quickly. TensorRT-LLM is better for teams running at massive scale where spending weeks on optimization saves thousands monthly in infrastructure costs.

What’s the difference between Ollama and llama.cpp?

Ollama is built on top of llama.cpp but adds a user-friendly layer with automatic model management, one-command installation, and simplified configuration. llama.cpp is the underlying inference engine that gives you more control but requires more manual setup. Think of Ollama as the Docker of LLM inference—optimized for developer experience. Use Ollama for quick prototyping, use llama.cpp directly when you need fine-grained control or CPU-optimized production deployment.

Which framework is fastest for LLM inference?

TensorRT-LLM can deliver the highest throughput (180-220 req/sec range) and lowest time-to-first-token (35-50ms) on supported NVIDIA hardware when properly configured. However, vLLM maintains better performance consistency under high concurrent load, keeping 50-80ms TTFT even with 100+ users. “Fastest” depends on your workload pattern—peak performance versus sustained performance under load—and proper configuration.

Do I need different frameworks for different regions?

No, the framework choice is the same globally, but regional constraints affect which framework is practical. Data sovereignty requirements in Singapore might push you toward regional cloud deployment. Hardware costs in India might make CPU-only inference with llama.cpp the only viable option. US companies often have easier access to GPU infrastructure but face competitive pressure for maximum performance. The technology is the same; the constraints differ.

How do I choose between cloud and on-premise deployment?

Cloud deployment (AWS, GCP, Azure) offers flexibility and faster scaling but with ongoing costs of $2,000-3,000 per GPU monthly. On-premise makes sense when you have sustained high load that justifies the $10,000-15,000 upfront GPU cost, or when regulatory requirements mandate keeping data in specific locations. Break-even is typically around 4-6 months of sustained usage. For startups and variable workloads, cloud is usually better. For established companies with predictable load, on-premise can be cheaper long-term.

What about quantization—do I need it?

Quantization (reducing model precision from 16-bit to 8-bit, 4-bit, or even 2-bit) is essential for running larger models on limited hardware. It can reduce memory requirements by 50-75% with minimal quality degradation. All modern frameworks support quantization, but llama.cpp has the most aggressive quantization options, making it possible to run 7B models on consumer CPUs. For GPU deployment, 4-bit or 8-bit quantization is standard practice for balancing performance and resource usage.

The Bottom Line

The framework landscape in 2025 is mature enough that you have real choices. vLLM for production serving, TensorRT-LLM for maximum performance, Ollama for prototyping, llama.cpp for resource-constrained deployment—each is legitimately good at what it does.

But the choice isn’t just technical. It’s about which constraints you’re operating under. A developer in Bangalore trying to build something profitable on a tight budget faces different constraints than a funded startup in San Francisco optimizing for scale. The “open” models are the same, but the paths to actually deploying them look completely different.

Here’s what I wish someone had told me when I started: don’t optimize for the perfect framework. Optimize for shipping something that works. Start with Ollama, prove your idea has value, then migrate to whatever framework makes sense for your scale and constraints. The best framework is the one that doesn’t stop you from building.

And if you’re choosing between a framework that requires GPUs you can’t afford versus llama.cpp on hardware you already have—choose llama.cpp. A working product beats a hypothetical perfect one every time.

The weights might be open, but the infrastructure isn’t equal. Choose the framework that works with your reality, not the one that works in someone else’s benchmarks.

Summarised in the following presentation deck:

References & Further Reading

Benchmark Studies & Performance Analysis

1. BentoML Team. “Benchmarking LLM Inference Backends: vLLM, LMDeploy, MLC-LLM, TensorRT-LLM, and Hugging Face TGI.” BentoML Blog. Retrieved from: https://www.bentoml.com/blog/benchmarking-llm-inference-backends
2. SqueezeBits Team. “[vLLM vs TensorRT-LLM] #1. An Overall Evaluation.” SqueezeBits Blog, October 2024. Retrieved from: https://blog.squeezebits.com/vllm-vs-tensorrtllm-1-an-overall-evaluation-30703
3. Clarifai. “Comparing SGLang, vLLM, and TensorRT-LLM with GPT-OSS-120B.” Clarifai Blog, September 2025. Retrieved from: https://www.clarifai.com/blog/comparing-sglang-vllm-and-tensorrt-llm-with-gpt-oss-120b
4. Clarifai. “LLM Inference Optimization Techniques.” Clarifai Guide, October 2025. Retrieved from: https://www.clarifai.com/blog/llm-inference-optimization/
5. ITECS Online. “vLLM vs Ollama vs llama.cpp vs TGI vs TensorRT-LLM: 2025 Guide.” October 2025. Retrieved from: https://itecsonline.com/post/vllm-vs-ollama-vs-llama.cpp-vs-tgi-vs-tensort

Framework Documentation & Official Sources

6. vLLM Project. “vLLM: High-throughput and memory-efficient inference and serving engine for LLMs.” GitHub Repository. Retrieved from: https://github.com/vllm-project/vllm
7. NVIDIA. “TensorRT-LLM Documentation.” NVIDIA Developer Documentation. Retrieved from: https://github.com/NVIDIA/TensorRT-LLM
8. Ollama Project. “Get up and running with large language models, locally.” Official Documentation. Retrieved from: https://ollama.ai/
9. llama.cpp Project. “LLM inference in C/C++.” GitHub Repository. Retrieved from: https://github.com/ggml-org/llama.cpp
10. Hugging Face. “Text Generation Inference Documentation.” Hugging Face Docs. Retrieved from: https://huggingface.co/docs/text-generation-inference/
11. SGLang Project. “SGLang: Efficient Execution of Structured Language Model Programs.” GitHub Repository. Retrieved from: https://github.com/sgl-project/sglang

Technical Analysis & Comparisons

12. Northflank. “vLLM vs TensorRT-LLM: Key differences, performance, and how to run them.” Northflank Blog. Retrieved from: https://northflank.com/blog/vllm-vs-tensorrt-llm-and-how-to-run-them
13. Inferless. “vLLM vs. TensorRT-LLM: In-Depth Comparison for Optimizing Large Language Model Inference.” Inferless Learn. Retrieved from: https://www.inferless.com/learn/vllm-vs-tensorrt-llm-which-inference-library-is-best-for-your-llm-needs
14. Neural Bits (Substack). “The AI Engineer’s Guide to Inference Engines and Frameworks.” August 2025. Retrieved from: https://multimodalai.substack.com/p/the-ai-engineers-guide-to-inference
15. The New Stack. “Six Frameworks for Efficient LLM Inferencing.” September 2025. Retrieved from: https://thenewstack.io/six-frameworks-for-efficient-llm-inferencing/
16. Zilliz Blog. “10 Open-Source LLM Frameworks Developers Can’t Ignore in 2025.” January 2025. Retrieved from: https://zilliz.com/blog/10-open-source-llm-frameworks-developers-cannot-ignore-in-2025

Regional Deployment & Cost Analysis

17. House of FOSS. “Ollama vs llama.cpp vs vLLM: Local LLM Deployment in 2025.” July 2025. Retrieved from: https://www.houseoffoss.com/post/ollama-vs-llama-cpp-vs-vllm-local-llm-deployment-in-2025
18. Picovoice. “llama.cpp vs. ollama: Running LLMs Locally for Enterprises.” July 2024. Retrieved from: https://picovoice.ai/blog/local-llms-llamacpp-ollama/
19. AWS Pricing. “Amazon EC2 P4d Instances (A100 GPU).” Retrieved Q4 2024 from: https://aws.amazon.com/ec2/instance-types/p4/
20. Google Cloud Pricing. “A2 VMs and GPUs pricing.” Retrieved Q4 2024 from: https://cloud.google.com/compute/gpus-pricing

Research Papers & Academic Sources

21. Kwon, Woosuk et al. “Efficient Memory Management for Large Language Model Serving with PagedAttention.” SOSP 2023. arXiv:2309.06180
22. Yu, Gyeong-In et al. “Orca: A Distributed Serving System for Transformer-Based Generative Models.” OSDI 2022.
23. NVIDIA Research. “TensorRT: High Performance Deep Learning Inference.” NVIDIA Technical Blog.

Community Resources & Tools

24. Awesome LLM Inference (GitHub). “A curated list of Awesome LLM Inference Papers with Codes.” Retrieved from: https://github.com/xlite-dev/Awesome-LLM-Inference
25. Hugging Face. “Introducing multi-backends (TRT-LLM, vLLM) support for Text Generation Inference.” Hugging Face Blog. Retrieved from: https://huggingface.co/blog/tgi-multi-backend
26. Sebastian Raschka. “Noteworthy LLM Research Papers of 2024.” January 2025. Retrieved from: https://sebastianraschka.com/blog/2025/llm-research-2024.html

Additional Technical Resources

27. Label Your Data. “LLM Inference: Techniques for Optimized Deployment in 2025.” December 2024. Retrieved from: https://labelyourdata.com/articles/llm-inference
28. Medium (Zain ul Abideen). “Best LLM Inference Engine? TensorRT vs vLLM vs LMDeploy vs MLC-LLM.” July 2024. Retrieved from: https://medium.com/@zaiinn440/best-llm-inference-engine-tensorrt-vs-vllm-vs-lmdeploy-vs-mlc-llm-e8ff033d7615
29. Rafay Documentation. “Choosing Your Engine for LLM Inference: The Ultimate vLLM vs. TensorRT LLM Guide.” April 2025. Retrieved from: https://docs.rafay.co/blog/2025/04/28/choosing-your-engine-for-llm-inference-the-ultimate-vllm-vs-tensorrt-llm-guide/
30. Hivenet Compute. “vLLM vs TGI vs TensorRT‑LLM vs Ollama.” Retrieved from: https://compute.hivenet.com/post/vllm-vs-tgi-vs-tensorrt-llm-vs-ollama

Survey Papers & Comprehensive Guides

31. Heisler, Morgan Lindsay et al. “LLM Inference Scheduling: A Survey of Techniques, Frameworks, and Trade-offs.” Huawei Technologies, 2025. Retrieved from: https://www.techrxiv.org/
32. Search Engine Land. “International SEO: Everything you need to know in 2025.” January 2025. Retrieved from: https://searchengineland.com/international-seo-everything-you-need-to-know-450866

Note on Sources

All benchmark figures, performance metrics, and pricing data cited in this guide were retrieved during Q4 2024 and early 2025. Framework capabilities, cloud pricing, and performance characteristics evolve rapidly in the LLM infrastructure space.

For the most current information:

• Check official framework documentation for latest features
• Verify cloud provider pricing in your specific region
• Run your own benchmarks with your specific workload
• Consult community forums (Reddit r/LocalLLaMA, Hacker News) for recent real-world experiences

Benchmark Reproducibility Note: Performance varies significantly based on:

• Exact framework versions used
• Model architecture and size
• Quantization settings
• Hardware configuration
• Batch size and concurrency patterns
• Prompt and completion lengths

The figures in this guide represent typical ranges observed across multiple independent benchmark studies. Your mileage will vary.

Acknowledgments

This guide benefited from:

• Public benchmark studies from BentoML, SqueezeBits, and Clarifai teams
• Open discussions in the vLLM, llama.cpp, and broader LLM communities
• Real-world deployment experiences shared by developers in India, Singapore, and US tech communities
• Technical documentation from framework maintainers and NVIDIA research

Special thanks to the open-source maintainers of vLLM, llama.cpp, Ollama, SGLang, and related projects who make this ecosystem possible.