StableBeluga2 — Hardware Requirements & GPU Compatibility
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StableBeluga2 is a 70B-parameter open language model from Stability AI. It supports a context window of up to 4,096 tokens. At Q4_K_M it needs about 42.97 GB of VRAM — see which GPUs and Macs can run it below.
580 downloads
881 likesSpecifications
- Publisher
- Stability AI
- Parameters
- 70B
- Architecture
- LlamaForCausalLM
- Context Length
- 4,096 tokens
- Vocabulary Size
- 32,000
- Release Date
- 2023-07-20
Get Started
HuggingFace
How Much VRAM Does StableBeluga2 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 30.7 GB | 31.4 GB | 29.75 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 31.6 GB | 32.3 GB | 30.63 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 35.1 GB | 35.8 GB | 34.13 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 36.0 GB | 36.6 GB | 35.00 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 43.0 GB | 43.6 GB | 42.00 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 50.9 GB | 51.5 GB | 49.88 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 58.7 GB | 59.4 GB | 57.75 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 71.0 GB | 71.6 GB | 70.00 GB | 8-bit quantization, near-lossless |
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 StableBeluga2?
Q4_K_M · 43.0 GBStableBeluga2 (Q4_K_M) requires 43.0 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 56+ GB is recommended. Using the full 4K context window can add up to 0.7 GB, bringing total usage to 43.6 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run StableBeluga2?
Q4_K_M · 43.0 GB27 devices with unified memory can run StableBeluga2, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio M4 Max (64 GB).
Runs great
— Plenty of headroom
Decent
— Enough memory, may be tightWhere to Download StableBeluga2
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Benchmarks
Benchmark details →Related Models
Frequently Asked Questions
- How much VRAM does StableBeluga2 need?
StableBeluga2 requires 43.0 GB of VRAM at Q4_K_M, or 141.0 GB at BF16. Full 4K context adds up to 0.7 GB (43.6 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 70B × 4.8 bits ÷ 8 = 42 GB
KV Cache + Overhead ≈ 1 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 1.6 GB (at full 4K context)
VRAM usage by quantization
Q4_K_M43.0 GBQ4_K_M + full context43.6 GB- Can NVIDIA GeForce RTX 4090 run StableBeluga2?
Yes, at IQ2_S (22.9 GB) or lower. Higher quantizations like IQ2_M (24.6 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for StableBeluga2?
For StableBeluga2, Q4_K_M (43.0 GB) offers the best balance of quality and VRAM usage. Q5_K_S (49.1 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 20.2 GB.
VRAM requirement by quantization
IQ2_XXS20.2 GBIQ3_XS29.9 GBQ3_K_M35.1 GBQ4_K_M ★43.0 GBQ5_K_S49.1 GBBF16141.0 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run StableBeluga2 on a Mac?
StableBeluga2 requires at least 20.2 GB at IQ2_XXS, 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 StableBeluga2 locally?
Yes — StableBeluga2 can run locally on consumer hardware. At Q4_K_M quantization it needs 43.0 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is StableBeluga2?
At Q4_K_M, StableBeluga2 can reach ~102 tok/s on AMD Instinct MI350X. 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 ÷ 43.0 × 0.65 = ~121 tok/s
Estimated speed at Q4_K_M (43.0 GB)
~121 tok/s~121 tok/s~102 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of StableBeluga2?
At Q4_K_M, the download is about 42.00 GB. The full-precision BF16 version is 140.00 GB. The smallest option (IQ2_XXS) is 19.25 GB.
- Which GPUs can run StableBeluga2?
No single consumer GPU has enough VRAM to run StableBeluga2 at Q4_K_M (43.0 GB). Multi-GPU or professional hardware is required.
- Which devices can run StableBeluga2?
27 devices with unified memory can run StableBeluga2 at Q4_K_M (43.0 GB), including ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), 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.