Serving Engines Compared

vLLM, SGLang, TRT-LLM and llama.cpp side by side. Schedulers, KV reuse, precision, offload and the pick.

Engines side by side#

Factor	vLLM	SGLang	TRT-LLM	llama.cpp
Core idea	PagedAttention, continuous batching, chunked prefill	RadixAttention, cache-aware scheduler	NVFP4 weights, FP8 KV, MTP	Fastest on CPU, widest model support
Scheduling	V1 scheduler, FCFS	Depth-first dispatch, branch-level LRU	Disaggregated prefill/decode	Full control over threading
Hardware	NVIDIA, AMD ROCm	NVIDIA, AMD	NVIDIA only, Blackwell	CPU, Apple Silicon Metal
Headline number	2,200-2,400 tok/s, 70B FP8, H100	~16,200 vs ~12,500 tok/s on 8B	$0.012/M tokens on GB200 NVL72	Ollama is 15-30% slower
Weight format	HF safetensors	HF safetensors	Compiled per GPU SKU	GGUF
Weak spot	Prefix-heavy traffic	Unique prompts, back to vLLM level	No AMD, no Intel, no ARM	Not the prod stage of the pipeline

while True:

finished = [s for s in RUNNING if s.is_done()]

for s in finished: release_blocks(s); RUNNING.remove(s)free blocks

while WAITING and have_free_blocks_for(WAITING[0]):admit on free KV

s = WAITING.pop(0)

allocate_initial_blocks(s)

RUNNING.append(s)

batch = []

for s in RUNNING:

if s.in_prefill:

batch.append(next_prefill_chunk(s))e.g. 512 tokens

else:

batch.append(decode_one_token(s))1 token

run_forward(batch)one fused GPU call

Factor	H100 + BF16 + vLLM	H100 + FP8 + vLLM	B200 + NVFP4 + FP8 + TRT-LLM	GB200 NVL72 + TRT-LLM + Dyn
HBM per GPU, GB	80	80	192	192
HBM bandwidth, TB/s	3.35	3.35	8.00	8.00
Weight bits	16	8	4	4
KV cache bits	16	8	8	8
MTP factor	1.0	1.0	1.8	1.8
Disaggregation factor	1.0	1.0	1.6	2.5
Price per GPU hour	2.50	2.50	4.80	6.20

Task	Code
Static batching	`simulate_static(reqs, batch=16)`
Continuous plus chunked	`simulate_continuous(reqs, chunked=True)`
Cache-aware radix order	`simulate(requests, "CACHE_AWARE")`
Scrambled prefix order	`workload_scrambled(n=80, docs=4)`
HBM footprint per stack	`hbm_footprint_gb(params_b, active_b, seq_len, stack)`
Cost per million tokens	`cost_per_million_tokens(active_b, stack)`
LMCache under HBM pressure	`simulate("LMCACHE", reqs)`
Engine for your constraints	`pick_engine(hardware, scale, workload)`

Symptom	Cause	Fix
Decode stalls during a long prompt	Prefill freezes decode	Chunked prefill. P99 ITL ~50 ms to ~15 ms
Hit rate 7% on shared prompts	Dynamic content in the prefix	Immutable parts first. 7% to 74%
6.4x gain gone	Template order changes per request	Fix one component order
HBM 100%, same prompt prefilled again	Preemption evicts KV	Native CPU offload or LMCache
3-5% slower with LMCache	Small HBM pressure	Turn LMCache off
Accuracy drops after NVFP4	FP4 weights on reasoning tasks	FP8 weights plus FP4 activations
Attention collapses at FP4 KV	KV needs dynamic range	Keep KV cache in FP8
Features break together	Combination not in the matrix	Check the matrix for your version

Why is FCFS wrong for prefix traffic?: It evicts the hot branch to admit a short-prefix request.
What does chunked prefill protect?: The TTFT tail and ITL, not mean throughput.
Where does the 7x TRT-LLM gap come from?: HBM bandwidth, NVFP4 weights, MTP and disaggregation.
LMCache for prompts under 1K tokens?: No. Transfer time is more than re-prefill.
Do you enable PagedAttention?: No flag. It is the only allocator vLLM ships.
Does vLLM reuse prefixes?: Yes, with --enable-prefix-caching. SGLang is radix-first.