llmgw

A single-binary LLM gateway for clusters with multiple InfiniBand fabrics and heterogeneous GPUs.

It serves LLMs running on your own GPU servers (vLLM / SGLang) behind one OpenAI-compatible endpoint. Design document: llmgw-plan.md.

Getting started

Starting from nothing — no packages installed, no drivers, no nodes — follow docs/install.md: it builds a control plane, adds a node and serves a model, and says plainly which parts llmgw does not install for you.

Guide	For
docs/install.md	Standing up a cluster from scratch
docs/node-onboarding.md	Adding nodes to one that exists
docs/onboarding.md	Giving people accounts and keys

Status

Releases track the roadmap phases; see CHANGELOG.md.

Version	Phase	Scope	State
v0.1.0	0	Skeleton and sim-engine	released
v0.2.0	1	Static router (filter/score/pick, streaming proxy)	released
v0.3.0	1.5	Admission control (priority bands)	released
v0.4.0	2	Agent: GPU/IB discovery, engine supervision	released
v0.5.0	3	Control plane: catalog, placement, reconciler, ctl	released
v0.6.0	4	Multi-node placement and fabric awareness	released
v1.0.0	5	Tenancy, prefix affinity, benchmarking	planned

As of v0.6.0 a cluster runs unattended: agents report their hardware, the control plane places replicas — including ones larger than a single machine — models load when someone asks for them and stop when nobody does, and keys, budgets and rate limits decide who may use any of it.

Before v1.0.0: routing profiles selectable per request, spillover to a fallback pool, tracing, and reference documentation. One thing is worth saying plainly: multi-node replicas are proven on a simulated cluster and on real hardware without a fabric. The InfiniBand, NVML and NCCL paths are written against the formats and failure modes those tools actually have, but they have not yet run on a machine that has them.

Building

make build      # bin/llmgw
make test       # unit tests
make race       # with the race detector

Operating a cluster

llmgw ctl nodes                      # capacity, health, what is running where
llmgw ctl models                     # catalog and replica counts
llmgw ctl replicas --model llama     # replicas and their shards
llmgw ctl explain-placement llama    # why a model has not been placed
llmgw ctl drain gpu-a-01             # move work off a node, one replica at a time
llmgw ctl cordon gpu-a-01            # stop new placement, leave what runs

ctl talks to the admin port (:9090 by default), not the client-facing one. Add --json to any command for machine-readable output.

explain-placement is the one worth knowing about: "unschedulable" on its own says nothing about which of six constraints bit, so it prints every candidate considered and the reason it was rejected.

Running the router

# Two fake engines and a router in front of them.
./bin/llmgw sim-engine --listen :8001 --model llama &
./bin/llmgw sim-engine --listen :8002 --model llama &
./bin/llmgw server \
  --endpoint e1=http://127.0.0.1:8001=llama \
  --endpoint e2=http://127.0.0.1:8002=llama

curl -N localhost:8080/v1/chat/completions \
  -H 'content-type: application/json' \
  -H 'x-session-id: demo' \
  -d '{"model":"llama","messages":[{"role":"user","content":"hello"}],"stream":true}'

For anything real use a config file; deploy/examples/server.yaml is commented throughout. Metrics, health and pprof are on the admin port (:9090 by default), deliberately separate from the client-facing one.

Requests carry a priority band (x-llmgw-band). Under load, low bands wait at the gateway while high bands keep flowing, so the cluster degrades from the bottom up instead of everywhere at once. Measured effect: interactive p99 stays within 1.24x of idle while 500 concurrent batch requests run, against 58x with admission off.

Routing profiles select how endpoints are scored: chat favours session affinity, batch favours the shortest queue, long-context favours free KV cache. Pick one per model in the config, or per request with the x-llmgw-profile header.

sim-engine

Simulates the inference-engine behaviours the router cares about, so the router can be developed and tested without GPUs. What it models:

• continuous batching (bounded number of concurrent sequences)
• KV cache block accounting, queueing once blocks run out (head-of-line blocking included)
• automatic prefix caching (chained block hashes plus an LRU) and its effect on time to first token
• per-token latency growing with batch size
• dropping a request and freeing its blocks when the client disconnects
• /metrics using vLLM's metric names

./bin/llmgw sim-engine --listen :8000 --model llama-3.3-70b

curl -N localhost:8000/v1/chat/completions \
  -H 'content-type: application/json' \
  -d '{"messages":[{"role":"user","content":"hello"}],"max_tokens":8,"stream":true}'

Tune load and latency behaviour with --max-num-seqs, --num-gpu-blocks, --prefill-tps, --decode-tps, --batch-contention and --prefix-cache-blocks. For chaos testing use --fail-rate, and --seed for deterministic runs.

The engine metric contract

For the router to make routing decisions an engine must expose these three metrics (plan §7.6). sim-engine publishes them under vLLM's real names:

vllm:num_requests_running
vllm:num_requests_waiting
vllm:kv_cache_usage_perc

Adding support for a new engine means satisfying that contract. Metric names drift between versions, so the name mapping is configurable.

Module path

gitea.mynodes.xyz/ilker/llmgw. To change it:

make rename NEW=git.example.com/team/llmgw

Language

Code, comments, tests, commit messages and documentation are in English.