Bigscience Bloom 560M — Hardware Requirements & GPU Compatibility
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Bigscience Bloom 560M is a 560M-parameter open language model from MisterAI. At Q4_K_M it needs about 0.37 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 · ~1771 tok/s · 32K)Macs from 8 GB (Q4_K_M · ~194 tok/s)
bigscience-bloom-rail-1.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Bigscience Bloom 560M
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve MisterAI/bigscience_bloom-560m --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 (10 GB headroom) · Q4_K_M
- Generation speed
- ~632 tok/s
- generation speed
- Cost per 1M output tokens
- $0.033
- per 1M output tokens
How Much VRAM Does Bigscience Bloom 560M Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 0.3 GB | 2.9 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 32K context · ~2520 tok/s | |
| 0.3 GB | 2.9 GB | 3-bit medium — 3-bit medium quantization | Fits: 32K context · ~2184 tok/s | |
| 0.4 GB | 3 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 32K context · ~1771 tok/s | |
| 0.4 GB | 3.1 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 32K context · ~1489 tok/s | |
| 0.5 GB | 3.1 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 32K context · ~1285 tok/s | |
| 0.6 GB | 3.2 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 32K context · ~1057 tok/s | |
| 1.2 GB | 3.8 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 32K context · ~533 tok/s |
Selected Q4_K_M
4-bit medium quantization1.6GBat 16K context
- Weights
- 0.1 GB
- KV cache
- 1.2 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 22.4 GB free · ~1771 tok/s
≥50 tok/s needs ≥28 GB/s · RTX 5090 ~3148, RTX 3090 Ti ~1771
Which Hardware Can Run Bigscience Bloom 560M?
Q4_K_M · 0.4 GBBigscience Bloom 560M (Q4_K_M) requires 0.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 1+ 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 Bigscience Bloom 560M, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 6.4 GB free · up to 32K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~1336Simulate output on RTX 3080RTX 2080 Ti~1082Simulate output on RTX 2080 TiRTX 3070 Ti~1069Simulate output on RTX 3070 TiM3*8 GB~194Simulate output on iMac 24" M3 (8 GB)Orin Nano~179Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~129Simulate output on iPhone 1720 GB
fits · 18.4 GB free · up to 32K ctx
1 GPU
RX 7900 XT~1297Simulate output on RX 7900 XT32–40 GB
fits · 30.4 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~3148Simulate output on RTX 5090M5 Max36 GB~870Simulate output on Mac Studio M5 Max (36 GB)M532 GB~291Simulate output on MacBook Air 13" M5 (32 GB)Orin~360Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~237Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Similar models
4| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| T5gemma B B Ul2 ITGoogle | — | 2.5 GB | ~504 | same size |
| Falcon H1 0.5B BaseTII UAE | — | 1.2 GB | ~950 | same size |
| Qwen3 0.6B BaseAlibaba | — | 1.6 GB | ~840 | same size |
| Xglm 564MMeta | — | 0.4 GB | ~1771 | same size |
Specifications
- Publisher
- MisterAI
- Parameters
- 560M
- Architecture
- BloomForCausalLM
- Vocabulary Size
- 250,880
- Release Date
- 2025-03-14
- License
- bigscience-bloom-rail-1.0
Frequently Asked Questions
How much VRAM does Bigscience Bloom 560M need?
Bigscience Bloom 560M requires 0.4 GB of VRAM at Q4_K_M, or 1.2 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 560M × 4.8 bits ÷ 8 = 0.3 GB
KV Cache + Overhead ≈ 0.1 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 Bigscience Bloom 560M?
For Bigscience Bloom 560M, Q4_K_M (0.4 GB) offers the best balance of quality and VRAM usage. Q5_K_M (0.4 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 0.3 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Bigscience Bloom 560M on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Bigscience Bloom 560M. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 0.4 GB of usable unified memory (RAM minus macOS overhead).
Can I run Bigscience Bloom 560M locally?
Yes — Bigscience Bloom 560M can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Bigscience Bloom 560M?
At Q4_K_M, Bigscience Bloom 560M can reach ~12973 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~1771 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 ÷ 0.37 × 0.65 = ~14054 tok/s
Estimated speed at Q4_K_M (0.4 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Bigscience Bloom 560M?
At Q4_K_M, the download is about 0.34 GB. The full-precision BF16 version is 1.12 GB. The smallest option (Q2_K) is 0.24 GB.
Which GPUs can run Bigscience Bloom 560M?
52 consumer GPUs can run Bigscience Bloom 560M 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 Bigscience Bloom 560M?
163 devices with unified memory can run Bigscience Bloom 560M at Q4_K_M (0.4 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.