Instinct Python Coder Gemma4 12B Qwen3.8 27B — Hardware Requirements & GPU Compatibility
ChatCodeReasoning
Instinct Python Coder Gemma4 12B Qwen3.8 27B is a 12B-parameter open language model from projectj in the Qwen 3.8 family. At Q4_K_M it needs about 7.92 GB of VRAM — see which GPUs and Macs can run it below.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 2K)Best on 24 GB (Q4_K_M · ~83 tok/s · 32K)Macs from 16 GB (Q4_K_M · ~18 tok/s)
Gemma Terms
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Instinct Python Coder Gemma4 12B Qwen3.8 27B
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
vllm serve projectj/Instinct-Python-Coder-Gemma4-12B-Qwen3.8-27B --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on RTX 5070 Ti (16 GB) (5 GB headroom) · Q4_K_M
- Generation speed
- ~74 tok/s
- generation speed
- Cost per 1M output tokens
- $0.64
- per 1M output tokens
How Much VRAM Does Instinct Python Coder Gemma4 12B Qwen3.8 27B Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 5.6 GB | 11.8 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 32K context · ~117 tok/s | |
| 6.4 GB | 12.6 GB | 3-bit medium — 3-bit medium quantization | Fits: 32K context · ~102 tok/s | |
| 7.9 GB | 14.1 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 32K context · ~83 tok/s | |
| 9.4 GB | 15.6 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 32K context · ~70 tok/s | |
| 10.9 GB | 17.1 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 32K context · ~60 tok/s | |
| 13.2 GB | 19.4 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 32K context · ~50 tok/s | |
| 26.4 GB | 32.6 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Does not fit: needs 29.3 GB |
Selected Q4_K_M
4-bit medium quantization10.8GBat 16K context
- Weights
- 7.2 GB
- KV cache
- 3.3 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 13.2 GB free · ~83 tok/s
≥50 tok/s needs ≥609 GB/s · RTX 5090 ~147, RTX 3090 Ti ~83
Which Hardware Can Run Instinct Python Coder Gemma4 12B Qwen3.8 27B?
Q4_K_M · 7.9 GBInstinct Python Coder Gemma4 12B Qwen3.8 27B (Q4_K_M) requires 7.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 11+ GB is recommended. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3080. 151 devices with unified memory can run Instinct Python Coder Gemma4 12B Qwen3.8 27B, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), NVIDIA Jetson Orin Nano 8GB (Super).
12 GB
12 GB tier →fits · 1.2 GB free · up to 16K ctx
10 GPUs
RTX 3080 Ti~75Simulate output on RTX 3080 TiRTX 5070~55Simulate output on RTX 5070RTX 4070~41Simulate output on RTX 407020 GB
fits · 9.2 GB free · up to 32K ctx
1 GPU
RX 7900 XT~61Simulate output on RX 7900 XT32–40 GB
fits · 21.2 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~147Simulate output on RTX 5090M5 Max36 GB~41Simulate output on Mac Studio M5 Max (36 GB)M532 GB~14Simulate output on MacBook Air 13" M5 (32 GB)Orin~17Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~11Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Instinct Python Coder Gemma4 12B Qwen3.8 27B
Qwen 3.8 family · 12 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Gemma 3 12B ITGoogle | 74.2 | 10.9 GB | ~82 | same size |
| Mellum2.1 12B A2.5B ThinkingJetBrains | — | 7.8 GB | ~257 | same size |
| Gemma 4 12BGoogle | — | 9 GB | ~80 | same size |
| Mistral Nemo Base 2407Mistral AI | — | 11 GB | ~81 | same size |
| Bielik 11B V3.0 Instructspeakleash | — | 27.4 GB | —needs > 24 GB | same size |
| NVIDIA Nemotron Nano 12B v2NVIDIA | — | 11 GB | ~81 | same size |
Specifications
- Publisher
- projectj
- Family
- Qwen 3.8
- Parameters
- 12B
- Release Date
- 2026-09-12
- License
- Gemma Terms
Frequently Asked Questions
How much VRAM does Instinct Python Coder Gemma4 12B Qwen3.8 27B need?
Instinct Python Coder Gemma4 12B Qwen3.8 27B requires 7.9 GB of VRAM at Q4_K_M, or 26.4 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 12B × 4.8 bits ÷ 8 = 7.2 GB
KV Cache + Overhead ≈ 0.7 GB (at 2K context + ~0.3 GB framework)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
VRAM usage by quantization
Can NVIDIA GeForce RTX 4090 run Instinct Python Coder Gemma4 12B Qwen3.8 27B?
Yes, at Q8_0 (13.2 GB) or lower. Higher quantizations like BF16 (26.4 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
What's the best quantization for Instinct Python Coder Gemma4 12B Qwen3.8 27B?
For Instinct Python Coder Gemma4 12B Qwen3.8 27B, Q4_K_M (7.9 GB) offers the best balance of quality and VRAM usage. Q5_K_M (9.4 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 5.6 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Instinct Python Coder Gemma4 12B Qwen3.8 27B on a Mac?
Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run Instinct Python Coder Gemma4 12B Qwen3.8 27B. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 7.9 GB of usable unified memory (RAM minus macOS overhead).
Can I run Instinct Python Coder Gemma4 12B Qwen3.8 27B locally?
Yes — Instinct Python Coder Gemma4 12B Qwen3.8 27B can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Instinct Python Coder Gemma4 12B Qwen3.8 27B?
At Q4_K_M, Instinct Python Coder Gemma4 12B Qwen3.8 27B can reach ~606 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~83 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 7.92 × 0.65 = ~657 tok/s
Estimated speed at Q4_K_M (7.9 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Instinct Python Coder Gemma4 12B Qwen3.8 27B?
At Q4_K_M, the download is about 7.20 GB. The full-precision BF16 version is 24.00 GB. The smallest option (Q2_K) is 5.10 GB.
Which GPUs can run Instinct Python Coder Gemma4 12B Qwen3.8 27B?
52 consumer GPUs can run Instinct Python Coder Gemma4 12B Qwen3.8 27B at Q4_K_M. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 36 GPUs have plenty of headroom for comfortable inference.
Which devices can run Instinct Python Coder Gemma4 12B Qwen3.8 27B?
151 devices with unified memory can run Instinct Python Coder Gemma4 12B Qwen3.8 27B at Q4_K_M (7.9 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.