IQuest Q1 — Hardware Requirements & GPU Compatibility
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IQuest-Q1 is IQuest's Mixture-of-Experts language model with 320 billion total and about 15 billion active parameters, built for agentic coding, reasoning and multi-step tool use. The card describes 88 layers, 256 experts with 8 active per token, a hybrid attention pattern of three sliding-window layers to one full-attention layer, and multi-token prediction layers. It is text-only and supports English and Chinese. Even quantized, the full weights need a multi-GPU server or a very large unified-memory machine, though only the active experts compute each token. The model supports a 524,288 token context window. It is released under the custom IQuest-Q1 license rather than a standard open-source license, so the terms in the repository's LICENSE file should be checked before commercial use. It was published in September 2026. The card compares it against DeepSeek-V4-Flash and DeepSeek-V4-Pro on agentic and coding benchmarks.
707 downloads
127 likesSpecifications
- Publisher
- IQuestLab
- Parameters
- 320.3B
- Architecture
- IQuestQ1ForCausalLM
- Context Length
- 524,288 tokens
- Vocabulary Size
- 160,000
- Release Date
- 2026-09-28
- License
- Other
Get Started
HuggingFace
How Much VRAM Does IQuest Q1 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 136.8 GB | 230.9 GB | 136.14 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 156.8 GB | 250.9 GB | 156.16 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 192.9 GB | 287.0 GB | 192.19 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 228.9 GB | 323.0 GB | 228.23 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 264.9 GB | 359.1 GB | 264.26 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 321.0 GB | 415.1 GB | 320.32 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 641.3 GB | 735.4 GB | 640.64 GB | Brain floating point 16 — preferred for training |
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 IQuest Q1?
Q4_K_M · 192.9 GBIQuest Q1 (Q4_K_M) requires 192.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 251+ GB is recommended. Using the full 524K context window can add up to 94.1 GB, bringing total usage to 287.0 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run IQuest Q1?
Q4_K_M · 192.9 GB3 devices with unified memory can run IQuest Q1, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomFrequently Asked Questions
- How much VRAM does IQuest Q1 need?
IQuest Q1 requires 192.9 GB of VRAM at Q4_K_M, or 641.3 GB at BF16. Full 524K context adds up to 94.1 GB (287.0 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 320.3B × 4.8 bits ÷ 8 = 192.2 GB
KV Cache + Overhead ≈ 0.7 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 94.8 GB (at full 524K context)
VRAM usage by quantization
Q4_K_M192.9 GBQ4_K_M + full context287.0 GB- Can NVIDIA GeForce RTX 5090 run IQuest Q1?
No — IQuest Q1 requires at least 136.8 GB at Q2_K, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- What's the best quantization for IQuest Q1?
For IQuest Q1, Q4_K_M (192.9 GB) offers the best balance of quality and VRAM usage. Q5_K_M (228.9 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 136.8 GB.
VRAM requirement by quantization
Q2_K136.8 GBQ4_K_M ★192.9 GBQ5_K_M228.9 GBQ6_K264.9 GBQ8_0321.0 GBBF16641.3 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run IQuest Q1 on a Mac?
IQuest Q1 requires at least 136.8 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 IQuest Q1 locally?
Yes — IQuest Q1 can run locally on consumer hardware. At Q4_K_M quantization it needs 192.9 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is IQuest Q1?
At Q4_K_M, IQuest Q1 can reach ~51 tok/s on AMD Instinct MI350X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B300 → 8000 ÷ 192.9 × 0.65 = ~152 tok/s
Estimated speed at Q4_K_M (192.9 GB)
~152 tok/s~51 tok/s~51 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of IQuest Q1?
At Q4_K_M, the download is about 192.19 GB. The full-precision BF16 version is 640.64 GB. The smallest option (Q2_K) is 136.14 GB.
- Which GPUs can run IQuest Q1?
No single consumer GPU has enough VRAM to run IQuest Q1 at Q4_K_M (192.9 GB). Multi-GPU or professional hardware is required.
- Which devices can run IQuest Q1?
4 devices with unified memory can run IQuest Q1 at Q4_K_M (192.9 GB), including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), NVIDIA DGX A100 640GB, NVIDIA DGX H100. Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.