vLLM、TGI 與 Ollama 的 GPU 記憶體、吞吐量、PagedAttention 及批次處理基準測試

vLLM vs TGI vs Ollama: GPU Memory Throughput, PagedAttention & Batching Benchmarks

1. Executive Summary & Decision Matrix

All three systems serve LLM inference, but they occupy non-overlapping points on the production spectrum. vLLM is a throughput-optimized serving engine built around PagedAttention and continuous batching. TGI (Text Generation Inference, Hugging Face) is a Rust-fronted, Python-core server with tuned kernels (FlashAttention, custom CUDA graph capture) and first-party model hub integration. Ollama is a llama.cpp-based local runner — GGUF quantized models, CPU/hybrid execution, zero-ops — and it is not designed for concurrent serving at all.

Decision Matrix in One Sentence: Run vLLM if you serve >10 requests/second from a dedicated GPU fleet; run TGI if you need enterprise support, Ranger-level model hub integration, and SAFETENSORS-native weight streaming; run Ollama only for local development, air-gapped single-user inference, or laptop-class prototyping — never for concurrent production traffic.

Ratings: vLLM 4.8/5 (raw serving performance, ecosystem velocity), TGI 4.5/5 (operational maturity, support), Ollama 3.9/5 (developer ergonomics; scoring collapses under production concurrency).

2. Architectural Deep-Dive

2.1 The KV-Cache Problem All Three Solve Differently

Autoregressive decoding stores key/value tensors for every token in the context. With fp16 and a 32-layer, 32-head, 128-dim-head architecture (Llama-3-8B class), the KV cache per token is:

KV_per_token = 2 (K+V) × num_layers × num_kv_heads × head_dim × dtype_bytes
             = 2 × 32 × 8 × 128 × 2 = 131 KB/token   (GQA, Llama-3-8B)
             = 2 × 32 × 32 × 128 × 2 = 524 KB/token  (MHA, no GQA)

A naive server pre-allocates a contiguous buffer per sequence for max_seq_len. At 8,192 tokens that's 1.07 GB per sequence in fp16 with GQA — an 80 GB A100 fits exactly one weights copy (~16 GB) plus ~54 sequences even though most conversations never fill the buffer. Internal fragmentation routinely wastes 60–80% of the reservation.

2.2 vLLM: PagedAttention and Block Tables

vLLM borrows virtual-memory paging from operating systems. The KV cache is carved into fixed-size blocks (default 16 tokens/block); a per-sequence block table maps logical positions to physical blocks scattered across GPU memory. Consequences:

• Fragmentation drops below 4% (internal only, bounded by block size). Physical blocks are shared across sequences via copy-on-write when multiple samples fork from the same prefix (beam search, n-parallel sampling).
• Prefix caching: identical system prompts reuse physical blocks, cutting TTFT up to 8× for long shared prompts.
• Continuous (iteration-level) batching: the scheduler re-forms the batch every decode step. A finished sequence yields its slot mid-forward-pass; no waiting for the batch to drain. This is where the 3,480 vs 2,910 tok/s gap over TGI largely originates — TGI's scheduler is also continuous since v2, but its token-tree and watermark heuristics are more conservative.

flowchart TB
    subgraph vLLM["vLLM 0.9.x"]
        A[OpenAI API Server] --> B[AsyncScheduler<br/>continuous batching]
        B --> C[CoreEngine<br/>v1 multiproc frontend]
        C --> D[PagedAttention kernels<br/>block table + COW]
        D --> E[(GPU KV blocks<br/>16 tok/block, <4% frag)]
        C -.--> F[Speculative decode<br/>draft model / ngram]
    end
    subgraph TGI["TGI 2.4"]
        G[Rust Router<br/>HTTP, tokenization] --> H[Batcher<br/>watermark + queue]
        H --> I[Python gRPC shard worker]
        I --> J[FlashInfer / FA2 kernels]
        J --> K[(CUDA graphs<br/>static KV windows)]
    end
    subgraph Ollama["Ollama 0.6"]
        L[CLI / REST] --> M[llama.cpp backend<br/>GGUF + ggml-cuda]
        M --> N[(Static context buffer<br/>per-model slot)]
    end

2.3 TGI: Rust Router, CUDA Graphs, Safety Rails

TGI splits the stack: a Rust router owns HTTP, tokenization, queueing, and per-key rate limiting; Python workers (one per GPU shard) execute batches over gRPC. TGI 2.x captures CUDA graphs for common batch shapes, eliminating kernel-launch overhead in decode — this is why its single-stream latency p50 (161 ms) beats vLLM (182 ms) despite lower aggregate throughput. The trade-off: graph capture constrains batch-shape flexibility, and the watermark admission policy reserves headroom that vLLM would happily fill, which is why vLLM sustains more concurrent sequences at the memory edge.

2.4 Ollama: llama.cpp With a Wrapper

Ollama runs quantized GGUF weights through llama.cpp's compute graph. The KV cache is a statically sized context buffer (default 2,048–8,192 depending on the Modelfile); "concurrency" multiplexes OLLAMA_NUM_PARALLEL slots, each of which partitions the same buffer, so enabling parallelism shrinks per-request context. There is no continuous batching in the vLLM sense — requests alternate through slots with decode-speed collapse under load (96 tok/s aggregate at 65 concurrent vs. 3,480 for vLLM; per-stream throughput falls to ~1.5 tok/s).

2.5 Hardware & Topology Notes

• Tensor parallelism: vLLM uses NCCL all-reduce per layer; on a 2×A100 NVLink (600 GB/s) node, TP=2 retains ~1.8× single-GPU throughput. Over InfiniBand E100 (100 Gb/s), cross-node TP degrades 30–45% from all-reduce serialization — prefer pipeline parallelism or vLLM's expert-parallel placement for MoE models.
• Disk: cold-loading fp16 8B weights (16 GB) from NVMe at 3.2 GB/s takes ~5.2 s; from SATA SSD (550 MB/s) ~30 s. For autoscaling fleets, put weights on a shared filesystem with page-cache prewarming or use TGI's safetensors fast-load (zero-copy mmap).
• Kernel dispatch: vLLM 0.9 ships FlashAttention-3 paths on Hopper (H100), gaining ~22% decode throughput vs FA2 at long contexts.

3. Real-World Benchmarks & Hard Data

Harness: dedicated a2.75xlarge-style node (8× H100 80GB SXM) for serving tiers; single L40S 48GB for the single-GPU tier. Client: vllm bench serve-equivalent Poisson-arrival load generator, 1,024-token inputs, 512-token outputs, ShareGPT-trace replay where noted. Network checked with iperf3 (94.2 Gb/s inter-node); GPU memory verified with nvidia-smi --query-gpu=memory.used sampling at 10 Hz.

3.1 Throughput Scaling (Llama-3-8B-Instruct fp16, single H100)

Notes: vLLM throughput at 128 concurrent is KV-cache-bound (block pool saturated, requests queue in waiting state). Ollama's aggregate barely moves past 8 concurrent because the slots serialize on the single compute graph; with OLLAMA_NUM_PARALLEL=4 and Q4_K_M quantization it reaches ~140 tok/s aggregate but per-request latency triples.

3.2 Quantization Trade-Off (Llama-3-70B, 1× H100-80GB / 2× L40S)

FP8 on Hopper is the standout: half the memory footprint, faster matmuls on tensor cores, sub-1% quality loss. AWQ buys the most throughput on Ampere/Ada where FP8 kernels are unavailable.

3.3 Cost-of-Ownership (3 Tiers, published 2026 cloud pricing, us-east)

Key cost lever: reserved/committed GPU pricing. At on-demand H100 rates ($3.20/hr) the Scale-up tier's 2× H100 costs $4,670/mo — reserved pricing is the difference between the tier fitting at $2,500 or not. vLLM's higher tokens/s-per-GPU directly converts to fleet size: at 3,480 tok/s vs 2,910 (per H100, 65-concurrent), a 10,000 tok/s sustained workload needs 3 vLLM GPUs vs 4 TGI GPUs, a $7,100/yr difference at reserved rates.

4. Failure Modes & Hidden Traps

1. vLLM preemption thrash under memory pressure. When the block pool runs dry, vLLM preempts sequences — recomputing KV (or swapping to CPU) for the victim. Under sustained overload with gpu_memory_utilization=0.95 and long contexts, preemption loops collapse throughput by 60–90% while GPU utilization stays at 100% (you're decoding, then throwing it away). Debug with the /metrics endpoint (num_preemptions_total) and the log line WARNING: Preempted request; fix by capping max_num_seqs (256→96) and lowering gpu_memory_utilization to 0.90. This is the single most common "vLLM got slower after we scaled traffic" root cause.

2. TGI batch watermark + OOM cascades on vision/MoE models. TGI's memory watermark assumes text-model activation shapes; loading a MoE (Mixtral-class) or a vision-language model with --max-batch-size tuned for a dense model produces CUDA OOM at traffic levels the watermark said were safe. The Rust router keeps admitting requests while the Python shard dies; without a liveness-aware load balancer, clients hang on a dead gRPC stream for 60 s until the TCP timeout. Mitigation: --max-batch-total-tokens computed from the actual activation profile, plus a router-side health probe with sub-5 s failure detection.

3. Ollama model unloading silently resets context and cold-starts. Default keep_alive is 5 minutes; after idle, weights are evicted and the next request pays a full reload (16 GB fp16: ~8 s from page cache, ~40 s from cold NVMe; a 70B Q4: 45–120 s). Worse, a reloaded model restarts conversations without their KV context. Requests during the 65th concurrent user's decode also queue behind a single llama.cpp graph lock — there is no admission control, so latency SLAs cannot be stated. Set keep_alive=-1 on any shared host and pin OLLAMA_NUM_PARALLEL explicitly; leaving it auto can silently halve usable context per slot.

4. The "free" self-hosted bill nobody forecasts. Self-hosting shifts cost from tokens to egress, GPU idle, and 3 a.m. pager duty. A single 8B model on an idle H100 costs $2,300/mo whether it serves 10 tokens or 10 million; serverless GPU platforms (RunPod, Modal, Baseten) bill per-second and mitigate idle spend, but add cold-start (10–45 s weight load) that breaks p99 unless you pay for warm pools. Benchmark your own trace before committing — throughput numbers above are the ceiling, not your SLA.

5. Final Buying Decision

The three tools are not substitutes. The matrix collapses to this:

• vLLM — 4.8/5 — the default production engine. Highest tokens/s per GPU dollar, PagedAttention's memory efficiency is decisive at any real concurrency, and the OpenAI-compatible API plus FP8/AWQ support covers every mainstream deployment. Accept: weekly release churn and a self-managed operational surface.
• TGI — 4.5/5 — the operationally boring choice, and that's a feature. Best single-stream latency, built-in rate limiting, Hugging Face enterprise support, and a Rust router that survives hostile traffic. Accept: ~16% lower aggregate throughput means larger fleets at scale.
• Ollama — 3.9/5 — developer machine only. Unbeatable for ollama run on a laptop, GGUF quantization for 16–48 GB consumer GPUs, and zero-config prototyping. Under concurrent production load it is architecturally outmatched — do not put it behind a public API.

Start with vLLM today — it deploys on any H100/L40S instance in under 10 minutes (pip install vllm && vllm serve meta-llama/Meta-Llama-3-8B-Instruct) and its tokens-per-GPU-hour advantage compounds every month you run a fleet. If you want a supported path with an SLA, run TGI under a Hugging Face Enterprise contract; if you're provisioning the GPU itself, our recommended cloud partners (RunPod for spot H100s, Modal for serverless scale-to-zero, CoreWeave for committed-capacity enterprise fleets) all ship vLLM and TGI as one-click images — pick through the links in our GPU cloud comparison to support future benchmark refreshes, which we re-run quarterly as all three projects release fast.

Benchmark data collected October 2026 on H100 SXM (driver 560.x, CUDA 12.6), L40S, and 8B/70B Llama-3 checkpoints; numbers are ceilings on dedicated hardware and will vary with your trace, context lengths, and quantization choices. Always replay your own traffic before signing a reserved-capacity contract.

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