Mamba 2.8B HF — Hardware Requirements & GPU Compatibility
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Mamba 2.8B HF is a 2.8B-parameter open language model from State Spaces. At Q4_K_M it needs about 1.83 GB of VRAM — see which GPUs and Macs can run it below.
8.2K downloads
122 likesSpecifications
- Publisher
- State Spaces
- Parameters
- 2.8B
- Architecture
- MambaForCausalLM
- Vocabulary Size
- 50,280
- Release Date
- 2024-03-05
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HuggingFace
How Much VRAM Does Mamba 2.8B HF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 1.3 GB | — | 1.18 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 1.5 GB | — | 1.35 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 1.8 GB | — | 1.66 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 2.2 GB | — | 1.97 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 2.5 GB | — | 2.28 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 3.0 GB | — | 2.77 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 6.1 GB | — | 5.54 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 Mamba 2.8B HF?
Q4_K_M · 1.8 GBMamba 2.8B HF (Q4_K_M) requires 1.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 3+ GB is recommended. 50 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Mamba 2.8B HF?
Q4_K_M · 1.8 GB59 devices with unified memory can run Mamba 2.8B HF, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Mamba 2.8B HF need?
Mamba 2.8B HF requires 1.8 GB of VRAM at Q4_K_M, or 6.1 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 2.8B × 4.8 bits ÷ 8 = 1.7 GB
KV Cache + Overhead ≈ 0.1 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M1.8 GB- What's the best quantization for Mamba 2.8B HF?
For Mamba 2.8B HF, Q4_K_M (1.8 GB) offers the best balance of quality and VRAM usage. Q5_K_M (2.2 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 1.3 GB.
VRAM requirement by quantization
Q2_K1.3 GBQ4_K_M ★1.8 GBQ5_K_M2.2 GBQ6_K2.5 GBQ8_03.0 GBBF166.1 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Mamba 2.8B HF on a Mac?
Mamba 2.8B HF requires at least 1.3 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 Mamba 2.8B HF locally?
Yes — Mamba 2.8B HF can run locally on consumer hardware. At Q4_K_M quantization it needs 1.8 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Mamba 2.8B HF?
At Q4_K_M, Mamba 2.8B HF can reach ~2404 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~358 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 ÷ 1.8 × 0.65 = ~2842 tok/s
Estimated speed at Q4_K_M (1.8 GB)
~2842 tok/s~358 tok/s~2842 tok/s~2404 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Mamba 2.8B HF?
At Q4_K_M, the download is about 1.66 GB. The full-precision BF16 version is 5.54 GB. The smallest option (Q2_K) is 1.18 GB.
- Which GPUs can run Mamba 2.8B HF?
50 consumer GPUs can run Mamba 2.8B HF at Q4_K_M (1.8 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 50 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Mamba 2.8B HF?
59 devices with unified memory can run Mamba 2.8B HF at Q4_K_M (1.8 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, 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.