Vicuna 7B Q8 — Hardware Requirements & GPU Compatibility
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Vicuna 7B Q8 is a 7B-parameter open language model from eccsm in the Vicuna family. At BF16 it needs about 15.40 GB of VRAM — see which GPUs and Macs can run it below.
Runs well on a 24 GB GPU
Runs from 16 GB (BF16 · 4K)Best on 24 GB (BF16 · ~43 tok/s · 32K)Macs from 24 GB (BF16 · ~14 tok/s)
MIT
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Vicuna 7B Q8
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
vllm serve eccsm/vicuna-7b-q8 --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on RTX 3090 (24 GB) (6 GB headroom) · BF16
- Generation speed
- ~40 tok/s
- generation speed
- Cost per 1M output tokens
- $1.39
- per 1M output tokens
How Much VRAM Does Vicuna 7B Q8 Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 15.4 GB | 20.7 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 32K context · ~43 tok/s |
Selected BF16
Brain floating point 1617.9GBat 16K context
- Weights
- 14.8 GB
- KV cache
- 2.8 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 6.1 GB free · ~43 tok/s
≥50 tok/s needs ≥1.18 TB/s · RTX 5090 ~76
Which Hardware Can Run Vicuna 7B Q8?
BF16 · 15.4 GBVicuna 7B Q8 (BF16) requires 15.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 21+ GB is recommended. 26 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 5080. 122 devices with unified memory can run Vicuna 7B Q8, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Mini M5 Pro (24 GB).
20 GB
fits · 2.1 GB free · up to 16K ctx
1 GPU
RX 7900 XT~31Simulate output on RX 7900 XT32–40 GB
fits · 14.1 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~76Simulate output on RTX 5090M5 Max36 GB~21Simulate output on Mac Studio M5 Max (36 GB)M532 GB~7Simulate output on MacBook Air 13" M5 (32 GB)Orin~9Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~6Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at BF16
Alternatives to Vicuna 7B Q8
Vicuna family · 3 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Olmo 3 7B InstructAllen AI | — | 8.4 GB | ~114 | same size |
| Mathstral 7B v0.1Mistral AI | — | 6.8 GB | ~133 | same size |
| Mistral 7B Instruct v0.3Mistral AI | — | 6.8 GB | ~133 | same size |
| Phi 3 Small 8k InstructMicrosoft | — | 16.2 GB | ~43 | same size |
| CodeQwen1.5 7BAlibaba | — | 5.7 GB | ~137 | same size |
| Starling LM 7B BetaNexusflow | — | 5.7 GB | ~133 | same size |
Specifications
- Publisher
- eccsm
- Family
- Vicuna
- Parameters
- 7B
- Release Date
- 2025-02-24
- License
- MIT
Frequently Asked Questions
How much VRAM does Vicuna 7B Q8 need?
Vicuna 7B Q8 requires 15.4 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 7B × 16 bits ÷ 8 = 14 GB
KV Cache + Overhead ≈ 1.4 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
Can I run Vicuna 7B Q8 on a Mac?
Yes — Mac Mini M5 Pro (24 GB) and 106 other Macs can run Vicuna 7B Q8. Apple Silicon uses unified memory, so the model shares RAM with the system. At BF16 you need at least 15.4 GB of usable unified memory (RAM minus macOS overhead).
Can I run Vicuna 7B Q8 locally?
Yes — Vicuna 7B Q8 can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Vicuna 7B Q8?
At BF16, Vicuna 7B Q8 can reach ~312 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~43 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 ÷ 15.4 × 0.65 = ~338 tok/s
Estimated speed at BF16 (15.4 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Vicuna 7B Q8?
At BF16, the download is about 14.00 GB.
Which GPUs can run Vicuna 7B Q8?
26 consumer GPUs can run Vicuna 7B Q8 at BF16. Top options include AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090, NVIDIA GeForce RTX 3090 Ti, AMD Radeon RX 6800. 7 GPUs have plenty of headroom for comfortable inference.
Which devices can run Vicuna 7B Q8?
122 devices with unified memory can run Vicuna 7B Q8 at BF16 (15.4 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.