Openchat 3.5 0106 — Hardware Requirements & GPU Compatibility
Chat
OpenChat-3.5-0106 is a 7.2-billion-parameter chat model fine-tuned from Mistral-7B-v0.1 using C-RLFT (Conditioned Reinforcement Learning Fine-Tuning), a method that lets the model learn from mixed-quality data by conditioning on data source rather than requiring uniformly high-quality demonstrations. At release, OpenChat billed it as the best-performing open 7B chat model, adding a dedicated coding mode alongside its generalist and math-reasoning mode plus experimental evaluator and feedback capabilities. At 7B parameters it runs on a single consumer GPU, and on modest cards once quantized. Context length is 8,192 tokens. It is released under the Apache 2.0 license, permitting unrestricted commercial and research use, and was published in January 2024, an update to the earlier OpenChat-3.5 and OpenChat-3.5-1210 checkpoints.
11.0K downloads
358 likesSpecifications
- Publisher
- OpenChat
- Family
- OpenChat
- Parameters
- 7.2B
- Architecture
- MistralForCausalLM
- Context Length
- 8,192 tokens
- Vocabulary Size
- 32,002
- Release Date
- 2024-01-07
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Openchat 3.5 0106 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 3.6 GB | 4.5 GB | 3.08 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 3.7 GB | 4.5 GB | 3.17 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 4.1 GB | 4.9 GB | 3.53 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 4.2 GB | 5.0 GB | 3.62 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 4.9 GB | 5.7 GB | 4.35 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 5.7 GB | 6.5 GB | 5.16 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 6.5 GB | 7.3 GB | 5.97 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 7.8 GB | 8.6 GB | 7.24 GB | 8-bit quantization, near-lossless |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run Openchat 3.5 0106?
Q4_K_M · 4.9 GBOpenchat 3.5 0106 (Q4_K_M) requires 4.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 7+ GB is recommended. Using the full 8K context window can add up to 0.8 GB, bringing total usage to 5.7 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Openchat 3.5 0106?
Q4_K_M · 4.9 GB59 devices with unified memory can run Openchat 3.5 0106, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Apple iPhone 17 Pro.
Runs great
— Plenty of headroom
Decent
— Enough memory, may be tightWhere to Download Openchat 3.5 0106
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Benchmarks
Benchmark details →Related Models
Frequently Asked Questions
- How much VRAM does Openchat 3.5 0106 need?
Openchat 3.5 0106 requires 4.9 GB of VRAM at Q4_K_M, or 15.1 GB at BF16. Full 8K context adds up to 0.8 GB (5.7 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 7.2B × 4.8 bits ÷ 8 = 4.3 GB
KV Cache + Overhead ≈ 0.6 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 1.4 GB (at full 8K context)
VRAM usage by quantization
Q4_K_M4.9 GBQ4_K_M + full context5.7 GB- What's the best quantization for Openchat 3.5 0106?
For Openchat 3.5 0106, Q4_K_M (4.9 GB) offers the best balance of quality and VRAM usage. Q5_0 (5.1 GB) provides better quality if you have the VRAM. The smallest option is IQ3_XS at 3.6 GB.
VRAM requirement by quantization
IQ3_XS3.6 GBIQ3_M3.8 GBIQ4_XS4.5 GBQ4_K_M ★4.9 GBQ5_K_S5.5 GBBF1615.1 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Openchat 3.5 0106 on a Mac?
Openchat 3.5 0106 requires at least 3.6 GB at IQ3_XS, 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 Openchat 3.5 0106 locally?
Yes — Openchat 3.5 0106 can run locally on consumer hardware. At Q4_K_M quantization it needs 4.9 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Openchat 3.5 0106?
At Q4_K_M, Openchat 3.5 0106 can reach ~978 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~133 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 ÷ 4.9 × 0.65 = ~1059 tok/s
Estimated speed at Q4_K_M (4.9 GB)
~1059 tok/s~133 tok/s~1059 tok/s~978 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Openchat 3.5 0106?
At Q4_K_M, the download is about 4.35 GB. The full-precision BF16 version is 14.48 GB. The smallest option (IQ3_XS) is 2.99 GB.
- Which GPUs can run Openchat 3.5 0106?
52 consumer GPUs can run Openchat 3.5 0106 at Q4_K_M (4.9 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 52 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Openchat 3.5 0106?
59 devices with unified memory can run Openchat 3.5 0106 at Q4_K_M (4.9 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.