OFFELLIA Gemma 4 E4B 8B Claude 4.6 Opus Reasoning MTP — Hardware Requirements & GPU Compatibility
ChatReasoning
OFFELLIA Gemma 4 E4B 8B Claude 4.6 Opus Reasoning MTP is a 4B-parameter open language model from Brunobkr in the Gemma 4 family. At Q4_K_M it needs about 2.64 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 · 32K)Best on 24 GB (Q4_K_M · ~248 tok/s · 32K)Macs from 8 GB (Q4_K_M · ~27 tok/s)
Other
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run OFFELLIA Gemma 4 E4B 8B Claude 4.6 Opus Reasoning MTP
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve Brunobkr/OFFELLIA_Gemma-4-E4B-8b-Claude-4.6-Opus-Reasoning-MTP --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Other ways
each opens step-by-stepRun in cloud
Fits on RTX 3060 12GB (7 GB headroom) · Q4_K_M
- Generation speed
- ~89 tok/s
- generation speed
- Cost per 1M output tokens
- $0.24
- per 1M output tokens
How Much VRAM Does OFFELLIA Gemma 4 E4B 8B Claude 4.6 Opus Reasoning MTP Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 1.9 GB | 6.4 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 32K context · ~350 tok/s | |
| 2.1 GB | 6.7 GB | 3-bit medium — 3-bit medium quantization | Fits: 32K context · ~305 tok/s | |
| 2.3 GB | 6.8 GB | 3-bit large — 3-bit large quantization | Fits: 32K context · ~290 tok/s | |
| 2.5 GB | 7 GB | 4-bit · i-quant non-linear — Importance-weighted 4-bit, non-linear | Fits: 32K context · ~264 tok/s | |
| 2.6 GB | 7.2 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 32K context · ~248 tok/s | |
| 3.1 GB | 7.7 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 32K context · ~209 tok/s | |
| 3.6 GB | 8.2 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 32K context · ~181 tok/s | |
| 4.4 GB | 8.9 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 32K context · ~149 tok/s | |
| 8.8 GB | 13.3 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 32K context · ~75 tok/s |
Selected Q4_K_M
4-bit medium quantization4.8GBat 16K context
- Weights
- 2.0 GB
- KV cache
- 2.4 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 19.3 GB free · ~248 tok/s
≥50 tok/s needs ≥203 GB/s · RTX 5090 ~441, RTX 3090 Ti ~248
Which Hardware Can Run OFFELLIA Gemma 4 E4B 8B Claude 4.6 Opus Reasoning MTP?
Q4_K_M · 2.6 GBOFFELLIA Gemma 4 E4B 8B Claude 4.6 Opus Reasoning MTP (Q4_K_M) requires 2.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 4+ GB is recommended. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run OFFELLIA Gemma 4 E4B 8B Claude 4.6 Opus Reasoning MTP, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 3.3 GB free · up to 32K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~187Simulate output on RTX 3080RTX 2080 Ti~152Simulate output on RTX 2080 TiRTX 3070 Ti~150Simulate output on RTX 3070 TiM3*8 GB~27Simulate output on iMac 24" M3 (8 GB)Orin Nano~25Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~18Simulate output on iPhone 1720 GB
fits · 15.3 GB free · up to 32K ctx
1 GPU
RX 7900 XT~182Simulate output on RX 7900 XT32–40 GB
fits · 27.3 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~441Simulate output on RTX 5090M5 Max36 GB~122Simulate output on Mac Studio M5 Max (36 GB)M532 GB~41Simulate output on MacBook Air 13" M5 (32 GB)Orin~50Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~33Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to OFFELLIA Gemma 4 E4B 8B Claude 4.6 Opus Reasoning MTP
Gemma 4 family · 35 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Gemma 3 4B ITGoogle | 64.0 | 5 GB | ~231 | same size |
| Granite Vision 4.1 4BIBM | — | 4 GB | ~228 | same size |
| Qwen3 4B Instruct 2507Alibaba | — | 4.2 GB | ~226 | same size |
| MiniCPM V 4OpenBMB | — | 3.1 GB | ~236 | same size |
| Phi 4 Mini ReasoningMicrosoft | — | 4.8 GB | ~228 | same size |
| Qwen2.5 VL 3B InstructAlibaba | — | 3.2 GB | ~249 | same size |
Specifications
- Publisher
- Brunobkr
- Family
- Gemma 4
- Parameters
- 4B
- Release Date
- 2026-07-11
- License
- Other
Frequently Asked Questions
How much VRAM does OFFELLIA Gemma 4 E4B 8B Claude 4.6 Opus Reasoning MTP need?
OFFELLIA Gemma 4 E4B 8B Claude 4.6 Opus Reasoning MTP requires 2.6 GB of VRAM at Q4_K_M, or 8.8 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 4B × 4.8 bits ÷ 8 = 2.4 GB
KV Cache + Overhead ≈ 0.2 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
What's the best quantization for OFFELLIA Gemma 4 E4B 8B Claude 4.6 Opus Reasoning MTP?
For OFFELLIA Gemma 4 E4B 8B Claude 4.6 Opus Reasoning MTP, Q4_K_M (2.6 GB) offers the best balance of quality and VRAM usage. Q5_K_M (3.1 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 1.9 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run OFFELLIA Gemma 4 E4B 8B Claude 4.6 Opus Reasoning MTP on a Mac?
Yes — Mac Mini M2 (8 GB) and 145 other Macs can run OFFELLIA Gemma 4 E4B 8B Claude 4.6 Opus Reasoning MTP. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 2.6 GB of usable unified memory (RAM minus macOS overhead).
Can I run OFFELLIA Gemma 4 E4B 8B Claude 4.6 Opus Reasoning MTP locally?
Yes — OFFELLIA Gemma 4 E4B 8B Claude 4.6 Opus Reasoning MTP can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is OFFELLIA Gemma 4 E4B 8B Claude 4.6 Opus Reasoning MTP?
At Q4_K_M, OFFELLIA Gemma 4 E4B 8B Claude 4.6 Opus Reasoning MTP can reach ~1818 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~248 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 ÷ 2.64 × 0.65 = ~1970 tok/s
Estimated speed at Q4_K_M (2.6 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of OFFELLIA Gemma 4 E4B 8B Claude 4.6 Opus Reasoning MTP?
At Q4_K_M, the download is about 2.40 GB. The full-precision BF16 version is 8.00 GB. The smallest option (Q2_K) is 1.70 GB.
Which GPUs can run OFFELLIA Gemma 4 E4B 8B Claude 4.6 Opus Reasoning MTP?
52 consumer GPUs can run OFFELLIA Gemma 4 E4B 8B Claude 4.6 Opus Reasoning MTP at Q4_K_M. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 52 GPUs have plenty of headroom for comfortable inference.
Which devices can run OFFELLIA Gemma 4 E4B 8B Claude 4.6 Opus Reasoning MTP?
163 devices with unified memory can run OFFELLIA Gemma 4 E4B 8B Claude 4.6 Opus Reasoning MTP at Q4_K_M (2.6 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.