The practical answer: vLLM is the general-purpose throughput leader for GPU production serving, TGI is the most opinionated, ops-friendly stack if you want a batteries-included server with tight Hugging Face integration, and Ollama is a developer-experience tool that trades throughput for simplicity and is rarely the right pick for sustained multi-user production load. The decisive differences come down to how each engine manages the KV-cache and batches requests, which is where most of your GPU bill is decided.
Why the Inference Engine Decides Your Production Bill
The engine is the software layer between your model weights and the GPU. Its job is to take incoming requests, decide which ones run together in a batch, allocate GPU memory for their intermediate state, and stream tokens back. Two engines running the same model on the same GPU can differ by a large multiple in how many tokens per second they serve to how many concurrent users — that ratio, not the GPU's raw speed, is what sets your cost per token.
The mechanism is utilization. A GPU is a fixed pool of compute and memory bandwidth. During autoregressive generation — producing one token at a time, each token depending on all previous ones — most requests spend their lives waiting: waiting for a batch slot, waiting for memory to be allocated, waiting for other requests to finish. An engine that keeps the GPU busy with useful work amortizes its cost across many users. An engine that serializes requests or wastes memory leaves the GPU idle while you still pay for it by the hour.
Concretely, suppose a GPU costs roughly a few dollars per hour (varies by provider and generation). If engine A serves 2,000 output tokens per second and engine B serves 500 on the same hardware, your cost per million output tokens differs by 4x: 4x the throughput means a quarter of the cost. That gap is typical between a naive sequential server and a properly batched one, though the exact ratio depends heavily on model size, sequence lengths, and concurrency. The lesson is that engine choice is a cost decision, not just a latency decision.
How vLLM, TGI, and Ollama Manage the KV-Cache
The KV-cache (key-value cache) is the memory holding the attention keys and values for every token already processed in a conversation. It exists so the model doesn't recompute the entire prompt for each new token. Its size is roughly:
bytes ≈ 2 (K and V) × layers × kv_heads × head_dim × seq_len × batch × dtype_bytes
For a mid-size model with long contexts and many concurrent requests, the KV-cache can exceed the memory used by the weights themselves. How an engine allocates this cache is the core architectural difference.
vLLM introduced PagedAttention, which stores the KV-cache in fixed-size blocks (pages) rather than one contiguous buffer per request. Blocks are allocated on demand and can be non-contiguous in memory, much like virtual memory in an operating system. This nearly eliminates internal fragmentation — wasted space from over-reserving for the maximum possible sequence length — and enables prefix sharing, where multiple requests with the same prompt reuse the same physical blocks. The practical effect is that more requests fit in memory at once, which is what unlocks large continuous batches.
TGI (Text Generation Inference) manages the KV-cache with pre-allocated per-request buffers sized to a configured maximum, plus its own optimizations (including paged attention in recent versions) and support for sharding a model across multiple GPUs. It is less flexible than vLLM's allocator in the general case but tightly tuned for the Hugging Face model ecosystem and common deployment shapes.
Ollama wraps llama.cpp and runs a small number of requests, typically one at a time per loaded model, with a simpler cache strategy and modest concurrency. That's fine for a laptop or a single developer, but it means the GPU sits largely idle under multi-user load — the opposite of what production needs. In short: vLLM and TGI are built to multiplex many requests over one GPU; Ollama is built to run one request well.
Throughput, Latency, and Batching Compared
Three metrics matter. Throughput is tokens served per second across all requests. Latency is how long one request waits for its first token (time to first token, TTFT) and for each subsequent token (inter-token latency, ITL). Batching is processing several requests in one forward pass, which is the main lever for throughput because a GPU is inefficient when it processes a single short sequence.
vLLM's scheduler is built around continuous batching: finished sequences leave the batch and queued ones join at every decode step, so the batch stays full instead of draining at a barrier. TGI offers continuous batching too, with its own queue and scheduler; it is a mature, well-instrumented server. Ollama serializes requests against one loaded model by default — its design goal is local, single-user convenience, not concurrent serving. Under many simultaneous users its queue grows roughly linearly with request count.
A worked estimate. Assume each decode step (one token for every sequence in the batch) costs about 30 ms on the GPU, dominated by weight reads and mostly insensitive to batch size up to a point. A batch of 16 sequences therefore produces 16 tokens per 30 ms:
throughput = 16 tokens / 0.030 s ≈ 530 tokens/s aggregate
per-request ITL = 30 ms → about 33 tokens/s per user
If the engine only runs one sequence at a time, aggregate throughput drops to roughly 33 tokens/s — the same GPU, about 16x less work done. That gap, not the model, is usually what separates a cheap deployment from an expensive one.
Deployment, Ops, and Choosing the Right Engine
vLLM ships as a Python package with an OpenAI-compatible server; you size --gpu-memory-utilization, --max-model-len, and tensor-parallel degree to your hardware. It gives the most knobs and the best raw throughput per GPU in most serving setups, at the cost of more tuning. TGI is a compiled Rust server (Hugging Face), deployed as a container or via their inference endpoints; it is production-hardened, with metrics and health checks out of the box. Ollama is a single binary with a model registry; you pull a model and it runs — excellent for laptops, prototypes, and internal tools, thin on multi-tenant isolation, autoscaling, and per-request quotas.
Deployment checklist:
- Measure TTFT and ITL under your real concurrency, not single-request speed.
- Cap
max-model-lenand concurrent sequences to fit the KV-cache; OOM under load is a capacity bug, not a model bug. - Expose Prometheus metrics and alert on queue depth and KV-cache usage.
- Load-test with your actual prompt/output length mix; throughput collapses when outputs are long.
- Keep a fallback: if the engine is saturated, shed or queue requests explicitly.
Rule of thumb: pick vLLM when throughput per GPU is the bill driver, TGI when you want a maintained server with fewer moving parts, and Ollama for development or single-user workloads — not multi-tenant production serving.
Frequently Asked Questions
What is the main difference between vLLM, TGI, and Ollama?
vLLM is an open-source serving engine built around PagedAttention, a KV-cache (the memory that stores past token states during generation) management technique borrowed from operating-system virtual memory, and it targets high-throughput GPU serving. TGI (Text Generation Inference), Hugging Face's production server, emphasizes broad model support, token streaming, and tight integration with the Hugging Face ecosystem plus features like tensor parallelism (splitting a model across several GPUs). Ollama is a developer-friendly local runtime that wraps models in a simple CLI and API, optimized for ease of setup on laptops and workstations rather than maximum concurrent throughput. In short: vLLM and TGI are production servers, Ollama is a convenience layer.
Which engine is fastest for high-throughput production serving?
For many-concurrent-request workloads on GPUs, vLLM is generally the throughput leader because PagedAttention reduces KV-cache fragmentation (wasted memory from variable-length sequences) and continuous batching lets new requests join a running batch instead of waiting for it to finish. TGI is competitive and often chosen when you need its model coverage or streaming features; exact rankings depend on model size, GPU type, sequence lengths, and quantization. Ollama is not designed for high-concurrency serving. Rather than trust any single claim, benchmark your own model and traffic shape; expect a gap of roughly 1.5x to 4x in requests per second between a tuned production engine and a naive local runtime, as an estimate.
Can Ollama handle production workloads?
It can serve real traffic for small teams, internal tools, or low-concurrency APIs, and it exposes an HTTP API that applications can call. But it lacks the batching efficiency, multi-GPU scaling, and operational controls (metrics, autoscaling hooks, fine-grained scheduling) that production engines provide, so throughput per GPU is typically much lower under load. For anything beyond modest concurrency, vLLM or TGI is the safer choice.
How does PagedAttention in vLLM improve KV-cache efficiency?
Traditionally each request pre-allocates a contiguous KV-cache block sized for the maximum sequence length, so most of that memory sits idle and blocks other requests. PagedAttention stores the cache in fixed-size blocks scattered across memory, tracked by a page table, so memory is allocated as tokens are actually generated. This cuts internal fragmentation, lets requests share blocks (for example, a common prompt prefix), and packs more concurrent sequences into the same GPU memory, which raises batch size and therefore throughput. Fewer wasted bytes mean more requests served per GPU.
Do I need GPUs to run these inference engines in production?
Not strictly, but GPUs matter enormously for latency and throughput. All three engines can run on CPU, and Ollama in particular is usable on CPU-only machines for small models, though token generation will be slow (often single-digit tokens per second for mid-size models, as an estimate). vLLM and TGI support CPU execution but are primarily designed for CUDA GPUs; TGI also supports other accelerators. For production serving of models beyond a few billion parameters under real concurrency, plan on GPUs.