Vicuna 13B V1.3 — Hardware Requirements & GPU Compatibility
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Vicuna 13B V1.3 is a 13B-parameter open language model from LMSYS in the Vicuna family. It supports a context window of up to 2,048 tokens. At FP16 it needs about 28.60 GB of VRAM — see which GPUs and Macs can run it below.
1.9K downloads
203 likesSpecifications
- Publisher
- LMSYS
- Family
- Vicuna
- Parameters
- 13B
- Architecture
- LlamaForCausalLM
- Context Length
- 2,048 tokens
- Vocabulary Size
- 32,000
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HuggingFace
How Much VRAM Does Vicuna 13B V1.3 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| FP16 | 16.00 | 28.6 GB | — | 26.00 GB | Full half-precision — baseline for inference |
Which GPUs Can Run Vicuna 13B V1.3?
FP16 · 28.6 GBVicuna 13B V1.3 (FP16) requires 28.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 38+ GB is recommended. 1 GPU can run it, including NVIDIA GeForce RTX 5090.
All compatible consumer-level GPUs are running near their VRAM limit. You may also want to consider professional GPUs (e.g., NVIDIA A100, H100) which offer significantly more VRAM. For more headroom and better throughput, consider a multi-GPU configuration with tensor parallelism (supported by tools like vLLM, llama.cpp, or text-generation-inference).
Decent
— Enough VRAM, may be tightWhich Devices Can Run Vicuna 13B V1.3?
FP16 · 28.6 GB15 devices with unified memory can run Vicuna 13B V1.3, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio M4 Max (36 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightBenchmarks
View all 1 →Related Models
Frequently Asked Questions
- How much VRAM does Vicuna 13B V1.3 need?
Vicuna 13B V1.3 requires 28.6 GB of VRAM at FP16.
VRAM = Weights + KV Cache + Overhead
Weights = 13B × 16 bits ÷ 8 = 26 GB
KV Cache + Overhead ≈ 2.6 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
FP1628.6 GB- Can I run Vicuna 13B V1.3 on a Mac?
Vicuna 13B V1.3 requires at least 28.6 GB at FP16, 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 13B V1.3 locally?
Yes — Vicuna 13B V1.3 can run locally on consumer hardware. At FP16 quantization it needs 28.6 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Vicuna 13B V1.3?
At FP16, Vicuna 13B V1.3 can reach ~102 tok/s on AMD Instinct MI300X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 28.6 × 0.55 = ~102 tok/s
Estimated speed at FP16 (28.6 GB)
~102 tok/s~76 tok/s~63 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Vicuna 13B V1.3?
At FP16, the download is about 26.00 GB.
- Which GPUs can run Vicuna 13B V1.3?
1 consumer GPU can run Vicuna 13B V1.3 at FP16 (28.6 GB). Top options include NVIDIA GeForce RTX 5090.
- Which devices can run Vicuna 13B V1.3?
15 devices with unified memory can run Vicuna 13B V1.3 at FP16 (28.6 GB), including Mac Mini M4 (32 GB), Mac Mini M4 Pro (48 GB), Mac Pro M2 Ultra (192 GB), Mac Studio M2 Ultra (192 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.