gemma-4-12B-it-inferentia2

Gemma-4 12B on AWS Inferentia2 — Tensor-Parallel (TP=2), device prefill

Compiled AWS Neuron artifacts that run Google's encoder-free google/gemma-4-12B-it across both NeuronCores of a single Inferentia2 device (inf2.8xlarge), with greedy decode token-for-token identical to the CPU reference and the prompt prefilled on-device (~0.1 s to first token).

This is a runnable inference port, not a fine-tune — the weights are Google's, unmodified. What's new is the tensor-parallel compilation recipe for the unified (encoder-free) 12B, which the vendor stack (optimum-neuron / NxD) does not currently provide.

TL;DR

Why this is its own port (vs the E2B/E4B builds)

Gemma-4 12B is the encoder-free unified multimodal model — it drops the separate audio/vision encoders and projects raw audio (640 samples / 40 ms) and image patches (48×48) straight into the LLM embedding space via lightweight linear layers. Its text config differs from the smaller Gemma-4 models in ways that matter for a Neuron port:

• 48 layers, hidden 3840, 16 attention / 8 KV heads, head_dim 256 (global layers 512), intermediate 15360, vocab 262144, softcap 30, tied embeddings.
• No Per-Layer Embeddings (hidden_size_per_layer_input=0) — unlike E2B/E4B.
• All 48 layers own their K/V (num_kv_shared_layers=0).
• attention_k_eq_v=true — global (full-attention) layers reuse K as V.
• num_global_key_value_heads=1 — global layers have a single KV head.
• sliding_window=1024 (2× the E4B window).

The recipe (what makes it fit + stay correct)

Built with NxD ModelBuilder: one bf16 sharded weight set is shared across a prefill bucket (seq = 64) and a decode bucket (seq = 1), with the KV cache device-resident via I/O aliasing — so weights are resident once (~12 GB/core) and the KV never leaves the cores. Three details are specific to the 12B and were required for correctness / to compile:

1. layer_scalar is a buffer that scales every layer's output; NxD weight-sharding loads parameters only, never buffers, so it must be copied from the checkpoint by hand — otherwise every layer over-scales and the output is garbage.
2. Global layers (KV heads = 1) don't divide across TP=2, so their K/V are kept replicated and num_key_value_groups is re-derived to match the per-rank sharded query heads.
3. Eager attention + no on-device softcap. The fused attention kernel overflows on-chip SRAM at sliding_window=1024, so the device model uses eager attention. The softcap (a tanh over the 262144-wide vocab in fp32) also overflows SRAM — but softcap is monotonic, so it's dropped from the device graph (greedy argmax is unchanged) and applied host-side only when sampling.

Files

Run

Prebuilt Docker image (bundles the compiled model + weights + server):

docker run -d --device /dev/neuron0 --ipc=host -p 8080:8080 \
  xbill9/gemma4-optb-12b:tp2-devprefill-256
curl -s localhost:8080/generate -d '{"prompt":"What is AWS Inferentia?","max_tokens":60}'

Serves OpenAI-compatible routes (/v1/chat/completions, /v1/completions) plus /generate, /health, /metrics.

Run on a single inf2.xlarge (¼ the price) — slim image. The inf2.xlarge has the same 2 NeuronCores as the inf2.8xlarge, just 16 GB host RAM instead of 128. gemma4_unified has no Per-Layer Embeddings, so the host only needs the scaled word embedding — the slim server (optb_server_slim.py) constructs the real Gemma4UnifiedTextScaledWordEmbedding and loads only embed_tokens.weight from safetensors, instead of materializing the ~24 GB transformer on the host:

docker run -d --device /dev/neuron0 --ipc=host -p 8080:8080 \
  xbill9/gemma4-optb-12b:slim-devprefill

Validated on an inf2.xlarge: coherent output ("The capital of France is Paris."), device prefill ~0.1 s, ~15 tok/s decode, ~8 GB host RAM (the same compiled mb_12b_256.pt runs on both cores). Add swap before first run — the neff load peaks host RAM on a 16 GB box.

From these files (on an inf2.8xlarge with the Neuron runtime, transformers==5.13.0, torch-neuronx==2.8.0, neuronx-distributed>=0.17):

# rebuild the device model (add ≥55 GB swap first — the 2-rank compile peaks past 128 GB RAM)
MODEL_DIR=./gemma-4-12B-it KV_MAX=256 KV_BUCKET=64 MB_WDTYPE=bf16 \
  MB_SAVE=./mb_12b_256.pt python tp_mb.py     # -> SEQ_MATCH True, "The capital of France is Paris."

# serve the saved model
MODEL_DIR=./gemma-4-12B-it MB_PATH=./mb_12b_256.pt KV_MAX=256 KV_BUCKET=64 \
  python optb_server_mb.py

Limitations

• 2 NeuronCores (TP=2) required — runs on inf2.8xlarge (full server) or inf2.xlarge (slim server, :slim-devprefill); bf16 is mandatory (fp32 = 24 GB/core, won't load).
• 256-token context. 12B leaves ~4 GB/core after weights, so the window is smaller than the E2B/E4B builds; a larger window needs a recompile and may not fit.
• Text generation only in this build — the audio/vision projection paths are not wired to the device model.
• Batch size 1, single-stream greedy/sampled decode.
• Compiled specifically for Inferentia2 + the pinned Neuron SDK versions.

License & attribution

• Base model: google/gemma-4-12B-it, © Google, Apache-2.0.
• These artifacts (compiled model + scripts) are Apache-2.0 derivatives; the serialized model embeds the base weights in bf16. Redistributed under Apache-2.0 with attribution to Google — include the upstream LICENSE/NOTICE when redistributing.

Not affiliated with or endorsed by Google or AWS. Provided for research/testing.