Longchat 7B V1.5 32k — Hardware Requirements & GPU Compatibility
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Longchat 7B V1.5 32k is a 6.7B-parameter open language model from LMSYS. It supports a context window of up to 4,096 tokens. At FP16 it needs about 14.85 GB of VRAM — see which GPUs and Macs can run it below.
2.9K downloads
61 likesSpecifications
- Publisher
- LMSYS
- Parameters
- 6.7B
- Architecture
- LlamaForCausalLM
- Context Length
- 4,096 tokens
- Vocabulary Size
- 32,000
- Release Date
- 2025-04-15
- License
- Apache 2.0
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HuggingFace
How Much VRAM Does Longchat 7B V1.5 32k Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| FP16 | 16.00 | 14.8 GB | 15.9 GB | 13.48 GB | Full half-precision — baseline for inference |
Which GPUs Can Run Longchat 7B V1.5 32k?
FP16 · 14.8 GBLongchat 7B V1.5 32k (FP16) requires 14.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 20+ GB is recommended. Using the full 4K context window can add up to 1.1 GB, bringing total usage to 15.9 GB. 17 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 5080.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Longchat 7B V1.5 32k?
FP16 · 14.8 GB27 devices with unified memory can run Longchat 7B V1.5 32k, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 (16 GB).
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Longchat 7B V1.5 32k need?
Longchat 7B V1.5 32k requires 14.8 GB of VRAM at FP16. Full 4K context adds up to 1.1 GB (15.9 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 6.7B × 16 bits ÷ 8 = 13.5 GB
KV Cache + Overhead ≈ 1.3 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 2.4 GB (at full 4K context)
VRAM usage by quantization
FP1614.8 GBFP16 + full context15.9 GB- Can I run Longchat 7B V1.5 32k on a Mac?
Longchat 7B V1.5 32k requires at least 14.8 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 Longchat 7B V1.5 32k locally?
Yes — Longchat 7B V1.5 32k can run locally on consumer hardware. At FP16 quantization it needs 14.8 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Longchat 7B V1.5 32k?
At FP16, Longchat 7B V1.5 32k can reach ~196 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~44 tok/s. 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 ÷ 14.8 × 0.55 = ~196 tok/s
Estimated speed at FP16 (14.8 GB)
~196 tok/s~44 tok/s~147 tok/s~121 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Longchat 7B V1.5 32k?
At FP16, the download is about 13.48 GB.
- Which GPUs can run Longchat 7B V1.5 32k?
17 consumer GPUs can run Longchat 7B V1.5 32k at FP16 (14.8 GB). Top options include AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090, NVIDIA GeForce RTX 3090 Ti, AMD Radeon RX 6800. 5 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Longchat 7B V1.5 32k?
27 devices with unified memory can run Longchat 7B V1.5 32k at FP16 (14.8 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.