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.
IQuest Coder V1 40B Loop Instruct runs on a 96 GB+ Mac or a workstation GPU
Best local pick → Mac Studio (M3 Ultra, 96GB) · BF16 · 85.3 GB · ~7 tok/s
Macs from 96 GB (BF16 · ~7 tok/s)
Other
Not set up for it?
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run IQuest Coder V1 40B Loop Instruct
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve IQuestLab/IQuest-Coder-V1-40B-Loop-Instruct --max-model-len 16384 --tensor-parallel-size 8vLLM repo with its own quantisation
Download: Weights on Hugging Face
Other ways
each opens step-by-stepRun in cloud
Fits on H200 (141 GB) (55 GB headroom) · BF16
- Generation speed
- ~39 tok/s
- generation speed
- Cost per 1M output tokens
- $25.77
- per 1M output tokens
How Much VRAM Does IQuest Coder V1 40B Loop Instruct Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | Runs on | |
|---|---|---|---|---|
| 80.6 GB | 90.6 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Runs on RTX PRO 6000 Blackwell Max-Q Workstation Edition 96 GB |
Selected BF16
Brain floating point 1685.3GBat 16K context
- Weights
- 79.6 GB
- KV cache
- 5.4 GB
- Overhead
- 0.3 GB
Server-class: ~85.3 GB at 16K context · one RTX PRO 6000 Blackwell Max-Q Workstation Edition 96 GB
Which Hardware 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 consumer GPU has enough memory. 30 devices with unified memory can run IQuest Coder V1 40B Loop Instruct, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Studio (M3 Ultra, 96GB).
96 GB
fits · 10.7 GB free · up to 32K ctx
3 GPUs · 2 Mac classes
RTX PRO 6000 Blackwell Max-Q Workstation Edition~15Simulate output on RTX PRO 6000 Blackwell Max-Q Workstation EditionRTX PRO 6000 Blackwell Workstation Edition~15Simulate output on RTX PRO 6000 Blackwell Workstation EditionRTX PRO 6000 Blackwell Server Edition~13Simulate output on RTX PRO 6000 Blackwell Server EditionM3 Ultra*96 GB~7Simulate output on Mac Studio (M3 Ultra, 96GB)M3 Max*96 GB~3Simulate output on MacBook Pro 14" M3 Max (96 GB)128–144 GB
fits · 42.7 GB free · up to 128K ctx
4 GPUs · 2 Mac classes · 4 device families
GH200 Grace Hopper Superchip~40Simulate output on GH200 Grace Hopper SuperchipH200 NVL~39Simulate output on H200 NVLH200 SXM~39Simulate output on H200 SXMM2 Ultra*128 GB~7Simulate output on Mac Pro M2 Ultra (128 GB)M5 Max128 GB~5Simulate output on Mac Studio M5 Max (128 GB)GB10~2Simulate output on ASUS Ascent GX10Jetson T5000 (Thor)~2Simulate output on NVIDIA Jetson AGX Thor Developer Kit
By VRAM tier · typical GPUs and Macs at BF16
Consumer GPUs (up to 32 GB) and Macs up to 64 GB: too small for this model (needs ≥ 85.3 GB)8 GB tier →12 GB tier →16 GB tier →24 GB tier →Macs →
Similar models
2| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Phi 3.5 MoE InstructMicrosoft | — | 23.9 GB | ~141 | same size |
| Falcon 40B InstructTII UAE | — | 23.8 GB | ~33 | same size |
Specifications
- Publisher
- IQuestLab
- Parameters
- 39.8B
- Architecture
- IQuestLoopCoderForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 76,800
- Release Date
- 2025-12-30
- License
- Other
Frequently 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)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 43.2 GB (at full 131K context)
VRAM usage by quantization
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?
Yes — Mac Studio (M3 Ultra, 96GB) and 20 other Macs can run IQuest Coder V1 40B Loop Instruct. Apple Silicon uses unified memory, so the model shares RAM with the system. At BF16 you need at least 80.6 GB of usable unified memory (RAM minus macOS overhead).
Can I run IQuest Coder V1 40B Loop Instruct locally?
Yes — IQuest Coder V1 40B Loop Instruct can run locally on consumer hardware. 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 ~60 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)
Real-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?
30 devices with unified memory can run IQuest Coder V1 40B Loop Instruct at BF16 (80.6 GB), including ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB), GMKtec EVO-X2 (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.