EuroLLM 9B Instruct 2512 — Hardware Requirements & GPU Compatibility
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EuroLLM 9B Instruct 2512 is a 9.2B-parameter open language model from utter-project. It supports a context window of up to 32,768 tokens. At Q4_K_M it needs about 6.14 GB of VRAM — see which GPUs and Macs can run it below.
6.9K downloads
10 likesSpecifications
- Publisher
- utter-project
- Parameters
- 9.2B
- Architecture
- LlamaForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 128,000
- Release Date
- 2026-01-26
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does EuroLLM 9B Instruct 2512 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 4.5 GB | 9.8 GB | 3.89 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 5.1 GB | 10.4 GB | 4.46 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 6.1 GB | 11.4 GB | 5.49 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 7.2 GB | 12.5 GB | 6.52 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 8.2 GB | 13.5 GB | 7.55 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 9.8 GB | 15.1 GB | 9.15 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 19.0 GB | 24.2 GB | 18.30 GB | Brain floating point 16 — preferred for training |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run EuroLLM 9B Instruct 2512?
Q4_K_M · 6.1 GBEuroLLM 9B Instruct 2512 (Q4_K_M) requires 6.1 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 8+ GB is recommended. Using the full 33K context window can add up to 5.3 GB, bringing total usage to 11.4 GB. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti.
Runs great
— Plenty of headroom
Which Devices Can Run EuroLLM 9B Instruct 2512?
Q4_K_M · 6.1 GB33 devices with unified memory can run EuroLLM 9B Instruct 2512, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Air 13" M3 (8 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does EuroLLM 9B Instruct 2512 need?
EuroLLM 9B Instruct 2512 requires 6.1 GB of VRAM at Q4_K_M, or 19.0 GB at BF16. Full 33K context adds up to 5.3 GB (11.4 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 9.2B × 4.8 bits ÷ 8 = 5.5 GB
KV Cache + Overhead ≈ 0.6 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 5.9 GB (at full 33K context)
VRAM usage by quantization
Q4_K_M6.1 GBQ4_K_M + full context11.4 GB- What's the best quantization for EuroLLM 9B Instruct 2512?
For EuroLLM 9B Instruct 2512, Q4_K_M (6.1 GB) offers the best balance of quality and VRAM usage. Q5_K_M (7.2 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 4.5 GB.
VRAM requirement by quantization
Q2_K4.5 GBQ4_K_M ★6.1 GBQ5_K_M7.2 GBQ6_K8.2 GBQ8_09.8 GBBF1619.0 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run EuroLLM 9B Instruct 2512 on a Mac?
EuroLLM 9B Instruct 2512 requires at least 4.5 GB at Q2_K, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run EuroLLM 9B Instruct 2512 locally?
Yes — EuroLLM 9B Instruct 2512 can run locally on consumer hardware. At Q4_K_M quantization it needs 6.1 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is EuroLLM 9B Instruct 2512?
At Q4_K_M, EuroLLM 9B Instruct 2512 can reach ~475 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~107 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 6.1 × 0.55 = ~475 tok/s
Estimated speed at Q4_K_M (6.1 GB)
~475 tok/s~107 tok/s~355 tok/s~294 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of EuroLLM 9B Instruct 2512?
At Q4_K_M, the download is about 5.49 GB. The full-precision BF16 version is 18.30 GB. The smallest option (Q2_K) is 3.89 GB.
- Which GPUs can run EuroLLM 9B Instruct 2512?
35 consumer GPUs can run EuroLLM 9B Instruct 2512 at Q4_K_M (6.1 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 28 GPUs have plenty of headroom for comfortable inference.
- Which devices can run EuroLLM 9B Instruct 2512?
33 devices with unified memory can run EuroLLM 9B Instruct 2512 at Q4_K_M (6.1 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.