Bloomz 560M — Hardware Requirements & GPU Compatibility
Chat
Bloomz 560M is a 559M-parameter open language model from BigScience. It supports a context window of up to 2,048 tokens. 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 · 2K)Best on 24 GB (Q4_K_M · ~1771 tok/s · 2K)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 Bloomz 560M
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
ollama run hf.co/mradermacher/bloomz-560m-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 23.6 GB free · 2K context
Download: bloomz-560m.Q4_K_M.gguf0.56 GBmradermacher
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on RTX 3080 Ti (12 GB) (11 GB headroom) · Q4_K_M
- Generation speed
- ~1603 tok/s
- generation speed
- Cost per 1M output tokens
- $0.021
- per 1M output tokens
How Much VRAM Does Bloomz 560M Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 0.3 GB | — | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 2K context · ~2520 tok/s | |
| 0.3 GB | — | 3-bit small — 3-bit small quantization | Fits: 2K context · ~2427 tok/s | |
| 0.3 GB | — | 3-bit medium — 3-bit medium quantization | Fits: 2K context · ~2184 tok/s | |
| 0.3 GB | — | 4-bit · legacy — 4-bit legacy quantization | Fits: 2K context · ~2114 tok/s | |
| 0.4 GB | — | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 2K context · ~1771 tok/s | |
| 0.4 GB | — | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 2K context · ~1489 tok/s | |
| 0.5 GB | — | 6-bit, very good — 6-bit quantization, very good quality | Fits: 2K context · ~1285 tok/s | |
| 0.6 GB | — | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 2K context · ~1057 tok/s |
Selected Q4_K_M
4-bit medium quantization0.4GBat 2K context
- Weights
- 0.1 GB
- KV cache
- 0.0 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 23.6 GB free · ~1771 tok/s
≥50 tok/s needs ≥28 GB/s · RTX 5090 ~3148, RTX 3090 Ti ~1771
Which Hardware Can Run Bloomz 560M?
Q4_K_M · 0.4 GBBloomz 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 Bloomz 560M, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 7.6 GB free · up to 2K 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 · 19.6 GB free · up to 2K ctx
1 GPU
RX 7900 XT~1297Simulate output on RX 7900 XT32–40 GB
fits · 31.6 GB free · up to 2K 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
- BigScience
- Parameters
- 559M
- Architecture
- BloomForCausalLM
- Context Length
- 2,048 tokens
- Vocabulary Size
- 250,880
- Release Date
- 2022-10-08
- License
- bigscience-bloom-rail-1.0
Frequently Asked Questions
How much VRAM does Bloomz 560M need?
Bloomz 560M requires 0.4 GB of VRAM at Q4_K_M, or 1.2 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 559M × 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 Bloomz 560M?
For Bloomz 560M, Q4_K_M (0.4 GB) offers the best balance of quality and VRAM usage. Q5_K_S (0.4 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 0.2 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Bloomz 560M on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Bloomz 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 Bloomz 560M locally?
Yes — Bloomz 560M can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Bloomz 560M?
At Q4_K_M, Bloomz 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 Bloomz 560M?
At Q4_K_M, the download is about 0.34 GB. The full-precision BF16 version is 1.12 GB. The smallest option (IQ2_XXS) is 0.15 GB.
Which GPUs can run Bloomz 560M?
52 consumer GPUs can run Bloomz 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 Bloomz 560M?
163 devices with unified memory can run Bloomz 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.