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.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 4K)Best on 24 GB (Q4_K_M · ~118 tok/s · 4K)Macs from 8 GB (IQ2_XXS · ~22 tok/s)
Llama 2 Community
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Vicuna 7B V1.5
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 vicuna:7b-v1.5-q4_K_MQ4_K_M fits fully on the RTX 4090 · 17.4 GB free · 4K context
Download: vicuna-7b-v1.5.Q4_K_M.gguf4.08 GBTheBloke
Ways to runOther ways
each opens step-by-stepRun in cloud
Your own private copy · Q4_K_M · 24 GB GPU
- The easiest way to try this model
- Running in a few minutes, easy setup
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Runs on RTX 3080 Ti (12 GB) (5 GB headroom) · Q4_K_M
- Generation speed
- ~107 tok/s
- generation speed
- Cost per 1M output tokens
- $0.32
- per 1M output tokens
How Much VRAM Does Vicuna 7B V1.5 Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 4.3 GB | — | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 4K context · ~151 tok/s | |
| 4.4 GB | — | 3-bit small — 3-bit small quantization | Fits: 4K context · ~148 tok/s | |
| 4.8 GB | — | 3-bit medium — 3-bit medium quantization | Fits: 4K context · ~137 tok/s | |
| 4.9 GB | — | 4-bit · legacy — 4-bit legacy quantization | Fits: 4K context · ~135 tok/s | |
| 5.6 GB | — | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 4K context · ~118 tok/s | |
| 6.4 GB | — | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 4K context · ~103 tok/s | |
| 7.2 GB | — | 6-bit, very good — 6-bit quantization, very good quality | Fits: 4K context · ~92 tok/s | |
| 8.4 GB | — | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 4K context · ~78 tok/s |
Selected Q4_K_M
4-bit medium quantization6.6GBat 4K context
- Weights
- 4.2 GB
- KV cache
- 2.1 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 17.4 GB free · ~118 tok/s
≥50 tok/s needs ≥428 GB/s · RTX 5090 ~209, RTX 3090 Ti ~118
Which Hardware 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. 151 devices with unified memory can run Vicuna 7B V1.5, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), NVIDIA Jetson Orin Nano 8GB (Super).
≤ 11 GB
≤ 11 GB tier →fits · 1.4 GB free · up to 4K ctx
16 GPUs · 1 device family
RTX 3080~89Simulate output on RTX 3080RTX 2080 Ti~72Simulate output on RTX 2080 TiRTX 3070 Ti~71Simulate output on RTX 3070 TiOrin Nano~12Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)20 GB
fits · 13.4 GB free · up to 4K ctx
1 GPU
RX 7900 XT~86Simulate output on RX 7900 XT32–40 GB
fits · 25.4 GB free · up to 4K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~209Simulate output on RTX 5090M5 Max36 GB~58Simulate output on Mac Studio M5 Max (36 GB)M532 GB~19Simulate output on MacBook Air 13" M5 (32 GB)Orin~24Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~16Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Vicuna 7B V1.5
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 |
Fine-tunes & variants(1)
Specifications
- 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
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)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 2.5 GB (at full 4K context)
VRAM usage by quantization
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 IQ2_XXS at 3.3 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Vicuna 7B V1.5 on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Vicuna 7B V1.5, the smallest at IQ2_XXS. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 5.6 GB of usable unified memory (RAM minus macOS overhead), from Mac Mini M2 Pro (16 GB).
Can I run Vicuna 7B V1.5 locally?
Yes — Vicuna 7B V1.5 can run locally on consumer hardware. 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 ÷ 5.57 × 0.65 = ~934 tok/s
Estimated speed at Q4_K_M (5.6 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 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 (IQ2_XXS) is 1.93 GB.
Which GPUs can run Vicuna 7B V1.5?
52 consumer GPUs can run Vicuna 7B V1.5 at Q4_K_M. 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?
151 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, 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.