Vicuna 7B V1.5 — Hardware Requirements & GPU Compatibility
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Vicuna 7B V1.5 is a 7B-parameter open language model from LMSYS in the Vicuna family. It supports a context window of up to 4,096 tokens. At Q4_K_M it needs about 5.57 GB of VRAM — see which GPUs and Macs can run it below.
56.0K downloads
403 likesSpecifications
- Publisher
- LMSYS
- Family
- Vicuna
- Parameters
- 7B
- Architecture
- LlamaForCausalLM
- Context Length
- 4,096 tokens
- Vocabulary Size
- 32,000
- Release Date
- 2023-07-29
- License
- Llama 2 Community
Get Started
HuggingFace
How Much VRAM Does Vicuna 7B V1.5 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 4.3 GB | 5.4 GB | 2.98 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 4.4 GB | 5.5 GB | 3.06 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 4.8 GB | 5.9 GB | 3.41 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 4.9 GB | 6.0 GB | 3.50 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 5.6 GB | 6.7 GB | 4.20 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 6.4 GB | 7.4 GB | 4.99 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 7.2 GB | 8.2 GB | 5.78 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 8.4 GB | 9.4 GB | 7.00 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Vicuna 7B V1.5?
Q4_K_M · 5.6 GBVicuna 7B V1.5 (Q4_K_M) requires 5.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 8+ GB is recommended. Using the full 4K context window can add up to 1.1 GB, bringing total usage to 6.7 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Vicuna 7B V1.5?
Q4_K_M · 5.6 GB58 devices with unified memory can run Vicuna 7B V1.5, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Air 13" M3 (8 GB).
Runs great
— Plenty of headroomWhere to Download Vicuna 7B V1.5
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Related Models
Frequently Asked Questions
- How much VRAM does Vicuna 7B V1.5 need?
Vicuna 7B V1.5 requires 5.6 GB of VRAM at Q4_K_M, or 15.4 GB at FP16. Full 4K context adds up to 1.1 GB (6.7 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 7B × 4.8 bits ÷ 8 = 4.2 GB
KV Cache + Overhead ≈ 1.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 2.5 GB (at full 4K context)
VRAM usage by quantization
Q4_K_M5.6 GBQ4_K_M + full context6.7 GB- What's the best quantization for Vicuna 7B V1.5?
For Vicuna 7B V1.5, Q4_K_M (5.6 GB) offers the best balance of quality and VRAM usage. Q5_0 (5.8 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 4.3 GB.
VRAM requirement by quantization
Q2_K4.3 GBQ4_04.9 GBQ4_K_M ★5.6 GBQ5_05.8 GBQ5_K_M6.4 GBFP1615.4 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Vicuna 7B V1.5 on a Mac?
Vicuna 7B V1.5 requires at least 4.3 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 Vicuna 7B V1.5 locally?
Yes — Vicuna 7B V1.5 can run locally on consumer hardware. At Q4_K_M quantization it needs 5.6 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Vicuna 7B V1.5?
At Q4_K_M, Vicuna 7B V1.5 can reach ~862 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~118 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 ÷ 5.6 × 0.65 = ~934 tok/s
Estimated speed at Q4_K_M (5.6 GB)
~934 tok/s~118 tok/s~934 tok/s~862 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Vicuna 7B V1.5?
At Q4_K_M, the download is about 4.20 GB. The full-precision FP16 version is 14.00 GB. The smallest option (Q2_K) is 2.98 GB.
- Which GPUs can run Vicuna 7B V1.5?
52 consumer GPUs can run Vicuna 7B V1.5 at Q4_K_M (5.6 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 40 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Vicuna 7B V1.5?
59 devices with unified memory can run Vicuna 7B V1.5 at Q4_K_M (5.6 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.