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Models / Gemma 4 31B

30.7B dense (no MoE) with hybrid attention - a 1024-token sliding window interleaved with full global attention across 60 layers - for a real 256K context. Multimodal (text + image). Apache 2.0.

The dense Gemma 4 sibling: more per-token quality than the 26B A4B MoE, at the cost of full 30.7B decode.

  • Q4_K_M (~18.3GB weights, ~20GB runtime) fits a 24GB 4090.
  • QAT (~19GB) is near-lossless at the same size.
  • Q8_0 (~33GB) needs 2x 3090 or 48GB.
  • BF16 (~61GB) needs a big rig.

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The provider reports that this model does not add embedded watermarks or provenance metadata to generated output.

Provider transparency docs →

coding reasoning writing chat general vision
Parameters
30.7B
Context
256k
License
apache 2.0
Developer
Google
Origin
🇺🇸 USA
Released
Apr 2026

Scores

Coding
86
Reasoning
89
General
85

Score per dollar

850 pts per $/M input

general_score (85) divided by cheapest input price ($0.10/M). Higher is better value. See live pricing.

Related models

Guides covering Gemma 4 31B

Save your hardware and every model page answers the real question: will it run on your machine, and how fast?

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Run it locally

Per-quant memory needs and a static "can you run it?" reference - no rig entry required

QAT
18.0GB 19.0GB min 22.0GB rec
Quantization-aware training - near-lossless at Q4 size
Q4_K_M
18.3GB 20.0GB min 22.0GB rec
Balanced - the usual local sweet spot
NVFP4
16.0GB 20.0GB min 22.0GB rec
NVIDIA FP4 - Blackwell or Apple MLX, near-lossless
Q8_0
32.6GB 34.0GB min 38.0GB rec
Near-lossless
BF16
61.4GB 63.0GB min 66.0GB rec
Full quality, largest

The reference hardware

Schematic of the NVIDIA Jetson Orin NX 16GB reference rig - 16GB unified memory, 102 GB/s aggregate bandwidth
Schematic of the Single GTX 1080 Ti (11GB) reference rig - 11GB VRAM, 484 GB/s aggregate bandwidth
Schematic of the 4x H100 80GB (320GB) reference rig - 320GB VRAM, 13400 GB/s aggregate bandwidth
Schematic of the NVIDIA DGX Station 748GB reference rig - 748GB unified memory, 8000 GB/s aggregate bandwidth
Schematic of the 4x RTX 5090 (128GB) reference rig - 128GB VRAM, 7168 GB/s aggregate bandwidth
Schematic of the 8x RTX 3090 rack (192GB) reference rig - 192GB VRAM, 7489 GB/s aggregate bandwidth
Schematic of the AMD Instinct MI300X (192GB) reference rig - 192GB VRAM, 5324 GB/s aggregate bandwidth
Schematic of the 4x RTX 4090 (96GB) reference rig - 96GB VRAM, 4032 GB/s aggregate bandwidth
Schematic of the 2x RTX 5090 (64GB) reference rig - 64GB VRAM, 3584 GB/s aggregate bandwidth
Schematic of the Single RTX 5090 (32GB) reference rig - 32GB VRAM, 1792 GB/s aggregate bandwidth
Schematic of the RTX PRO 6000 Blackwell (96GB) reference rig - 96GB VRAM, 1792 GB/s aggregate bandwidth
Schematic of the 2x RTX 3090 (48GB) reference rig - 48GB VRAM, 1872 GB/s aggregate bandwidth
Schematic of the Mac Studio M4 Ultra 192GB reference rig - 192GB unified memory, 1092 GB/s aggregate bandwidth
Schematic of the Mac Studio M4 Ultra 512GB reference rig - 512GB unified memory, 1092 GB/s aggregate bandwidth
Schematic of the Single RTX 4090 (24GB) reference rig - 24GB VRAM, 1008 GB/s aggregate bandwidth
Schematic of the MacBook Pro M5 Max 128GB reference rig - 128GB unified memory, 614 GB/s aggregate bandwidth
Schematic of the Dual EPYC 9004 + 768GB DDR5-4800 reference rig - 768GB unified memory, 460 GB/s aggregate bandwidth
Schematic of the DGX Spark 128GB unified reference rig - 128GB unified memory, 273 GB/s aggregate bandwidth
Schematic of the Ryzen AI Max+ 395 128GB reference rig - 128GB unified memory, 256 GB/s aggregate bandwidth
Schematic of the Jetson AGX Orin 64GB reference rig - 64GB unified memory, 204 GB/s aggregate bandwidth
Schematic of the Epyc + 512GB DDR4-3200 + 2x RTX 3090 reference rig - 560GB unified memory, 204 GB/s aggregate bandwidth
Schematic of the Epyc + 512GB DDR4-2400 + 2x RTX 3090 reference rig - 560GB unified memory, 153 GB/s aggregate bandwidth

22 reference configs, drawn in-house. Scroll for more.

Can you run it? - reference rigs

Rig QAT Q4_K_M NVFP4 Q8_0 BF16
NVIDIA Jetson Orin NX 16GB no -> cloud no -> cloud no -> cloud no -> cloud no -> cloud
Single GTX 1080 Ti (11GB) no -> cloud no -> cloud no -> cloud no -> cloud no -> cloud
4x H100 80GB (320GB) fast 346.4t/s fast 341.6t/s no -> cloud fast 205.4t/s fast 114.0t/s
NVIDIA DGX Station 748GB fast 206.8t/s fast 203.9t/s no -> cloud fast 122.6t/s fast 68.0t/s
4x RTX 5090 (128GB) fast 185.3t/s fast 182.7t/s fast 204.5t/s fast 109.9t/s fast 61.0t/s
8x RTX 3090 rack (192GB) fast 193.6t/s fast 190.9t/s no -> cloud fast 114.8t/s fast 63.7t/s
AMD Instinct MI300X (192GB) fast 137.7t/s fast 135.7t/s no -> cloud fast 81.6t/s fast 45.3t/s
4x RTX 4090 (96GB) fast 104.2t/s fast 102.8t/s no -> cloud fast 61.8t/s fast 34.3t/s
2x RTX 5090 (64GB) fast 92.7t/s fast 91.4t/s fast 102.3t/s fast 54.9t/s tight
Single RTX 5090 (32GB) fast 46.3t/s fast 45.7t/s fast 51.1t/s offload no -> cloud
RTX PRO 6000 Blackwell (96GB) fast 46.3t/s fast 45.7t/s fast 51.1t/s fast 27.5t/s ok 15.2t/s
2x RTX 3090 (48GB) fast 48.4t/s fast 47.7t/s no -> cloud fast 28.7t/s offload
Mac Studio M4 Ultra 192GB fast 30.8t/s fast 30.4t/s fast 34.0t/s ok 18.3t/s ok 10.1t/s
Mac Studio M4 Ultra 512GB fast 30.8t/s fast 30.4t/s fast 34.0t/s ok 18.3t/s ok 10.1t/s
Single RTX 4090 (24GB) fast 26.1t/s fast 25.7t/s no -> cloud offload no -> cloud
MacBook Pro M5 Max 128GB ok 17.3t/s ok 17.1t/s ok 19.1t/s ok 10.3t/s slow 5.7t/s
Dual EPYC 9004 + 768GB DDR5-4800 ok 11.9t/s ok 11.8t/s no -> cloud slow 7.1t/s slow 3.9t/s
DGX Spark 128GB unified slow 7.1t/s slow 7.0t/s slow 7.8t/s slow 4.2t/s slow 2.3t/s
Ryzen AI Max+ 395 128GB slow 6.6t/s slow 6.5t/s no -> cloud slow 3.9t/s slow 2.2t/s
Jetson AGX Orin 64GB slow 5.3t/s slow 5.2t/s no -> cloud slow 3.1t/s tight
Epyc + 512GB DDR4-3200 + 2x RTX 3090 slow 5.3t/s slow 5.2t/s no -> cloud slow 3.1t/s slow 1.7t/s
Epyc + 512GB DDR4-2400 + 2x RTX 3090 slow 4.0t/s slow 3.9t/s no -> cloud slow 2.4t/s slow 1.3t/s

Fit tiers use the same will-it-run logic as the rig finder. For comfortable fits, the badge reflects decode speed: fast >=20 t/s, ok 8-20 t/s, slow <8 t/s. t/s is a bandwidth estimate, not a measured benchmark.

Download options

QAT official -1% vs fp16
18.0GB dl 19.0GB min 22.0GB rec
REC RAM vs largest quant
Q4_K_M official -5% vs fp16
18.3GB dl 20.0GB min 22.0GB rec
REC RAM vs largest quant
NVFP4 official -1% vs fp16
16.0GB dl 20.0GB min 22.0GB rec
REC RAM vs largest quant
NVFP4 - NVIDIA Blackwell via vLLM or Apple Silicon via MLX (ollama run gemma4:31b-nvfp4). Not supported on Ada/Ampere/Hopper/AMD.
Q8_0 official -1% vs fp16
32.6GB dl 34.0GB min 38.0GB rec
REC RAM vs largest quant
BF16 official
61.4GB dl 63.0GB min 66.0GB rec
REC RAM vs largest quant
GGUF on HF →

Or run it in the cloud

Live per-provider pricing, throughput and uptime - refreshed about 11 hours ago via OpenRouter. Click a column to sort.

some pricing may be stale - last verified 2026-11-13

Provider Type Input $/M Output $/M Cache $/M Tok/s Latency Uptime Value
CoreWeave
API 0.10 0.34 0.100 - - 100.00% best uptime
KT
API 0.10 0.34 0.100 - - 100.00%
Venice
API 0.12 0.36 0.090 - - 100.00%
Chutes
API 0.12 0.37 0.012 - - 100.00%
Novita
API 0.14 0.40 - - - 100.00%
Crusoe
API 0.14 0.40 0.140 - - 100.00%
Friendli
API 0.14 0.40 - - - 100.00%
Parasail
API 0.15 0.40 0.060 - - 100.00%
NextBit
API 0.15 0.40 0.060 - - 100.00%
PrimeIntellect
API 0.20 0.34 0.100 - - 100.00%
DeepInfra
API 0.30 0.60 - - - 100.00%
API 0.34 0.10 0.100 - - 100.00%
Together AI stale
API 0.39 0.97 - - - -
SiliconFlow
API 0.75 1.00 0.250 - - 100.00%
SambaNova risky
API 0.38 1.15 - - - 90.00%
DekaLLM avoid
API 0.14 0.40 0.050 - - 83.33%

Default order: throughput among 95%+ uptime providers, then latency; subscriptions last. Sort by any column. Subscription rows show $/mo in the Value column - per-token columns are "-". Affiliate links are marked sponsored / nofollow. Confirm current pricing on the provider's site before committing.

Detailed API pricing page + JSON endpoint →

See who runs Google in production →

PRICE HISTORY

Inference cost over time

Data accumulates from the first daily sync - longer ranges populate over time. Prices come from OpenRouter snapshots, not a historical API.

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