IQuest Coder V1 40B Loop Instruct — Hardware Requirements & GPU Compatibility
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IQuest Coder V1 40B Loop Instruct is a 39.8B-parameter open language model from IQuestLab. It supports a context window of up to 131,072 tokens. At BF16 it needs about 80.56 GB of VRAM — see which GPUs and Macs can run it below.
8.6K downloads
322 likesSpecifications
- Publisher
- IQuestLab
- Parameters
- 39.8B
- Architecture
- IQuestLoopCoderForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 76,800
- Release Date
- 2025-12-30
- License
- Other
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HuggingFace
How Much VRAM Does IQuest Coder V1 40B Loop Instruct Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 80.6 GB | 122.8 GB | 79.59 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 Coder V1 40B Loop Instruct?
BF16 · 80.6 GBIQuest Coder V1 40B Loop Instruct (BF16) requires 80.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 105+ GB is recommended. Using the full 131K context window can add up to 42.3 GB, bringing total usage to 122.8 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run IQuest Coder V1 40B Loop Instruct?
BF16 · 80.6 GB18 devices with unified memory can run IQuest Coder V1 40B Loop Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, ASUS Ascent GX10.
Runs great
— Plenty of headroom
Decent
— Enough memory, may be tightFrequently Asked Questions
- How much VRAM does IQuest Coder V1 40B Loop Instruct need?
IQuest Coder V1 40B Loop Instruct requires 80.6 GB of VRAM at BF16. Full 131K context adds up to 42.3 GB (122.8 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 39.8B × 16 bits ÷ 8 = 79.6 GB
KV Cache + Overhead ≈ 1 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 43.2 GB (at full 131K context)
VRAM usage by quantization
BF1680.6 GBBF16 + full context122.8 GB- Can NVIDIA GeForce RTX 5090 run IQuest Coder V1 40B Loop Instruct?
No — IQuest Coder V1 40B Loop Instruct requires at least 80.6 GB at BF16, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- Can I run IQuest Coder V1 40B Loop Instruct on a Mac?
IQuest Coder V1 40B Loop Instruct requires at least 80.6 GB at BF16, 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 Coder V1 40B Loop Instruct locally?
Yes — IQuest Coder V1 40B Loop Instruct can run locally on consumer hardware. At BF16 quantization it needs 80.6 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is IQuest Coder V1 40B Loop Instruct?
At BF16, IQuest Coder V1 40B Loop Instruct can reach ~55 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 B200 → 8000 ÷ 80.6 × 0.65 = ~65 tok/s
Estimated speed at BF16 (80.6 GB)
~65 tok/s~65 tok/s~55 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of IQuest Coder V1 40B Loop Instruct?
At BF16, the download is about 79.59 GB.
- Which GPUs can run IQuest Coder V1 40B Loop Instruct?
No single consumer GPU has enough VRAM to run IQuest Coder V1 40B Loop Instruct at BF16 (80.6 GB). Multi-GPU or professional hardware is required.
- Which devices can run IQuest Coder V1 40B Loop Instruct?
19 devices with unified memory can run IQuest Coder V1 40B Loop Instruct at BF16 (80.6 GB), including ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), 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.