INTELLECT 1 Instruct — Hardware Requirements & GPU Compatibility
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INTELLECT-1-Instruct is the instruction-tuned version of INTELLECT-1, a 10-billion-parameter language model that Prime Intellect describes as the first collaboratively, globally distributed pretraining run of its scale: 1 trillion tokens of English text and code trained across up to 14 concurrent nodes on three continents contributed by 30 independent community participants, using the DiLoCo distributed-training algorithm. Post-training was handled by Arcee AI through supervised fine-tuning, eight rounds of direct preference optimization, and model merging, using the Llama-3 tokenizer and logits distilled from Llama-3.1-405B to improve alignment. At 10B parameters it fits a single consumer GPU once quantized, more comfortably at full precision on a higher-end card. 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 November 2024, alongside the base INTELLECT-1 model.
848 downloads
125 likesSpecifications
- Publisher
- PrimeIntellect
- Parameters
- 10.2B
- Architecture
- LlamaForCausalLM
- Context Length
- 8,192 tokens
- Vocabulary Size
- 128,256
- Release Date
- 2024-11-28
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does INTELLECT 1 Instruct Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 5.0 GB | 6.0 GB | 4.34 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 5.1 GB | 6.2 GB | 4.47 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 5.6 GB | 6.7 GB | 4.98 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 5.8 GB | 6.8 GB | 5.11 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 6.8 GB | 7.8 GB | 6.13 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 7.9 GB | 9.0 GB | 7.28 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 9.1 GB | 10.1 GB | 8.42 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 10.9 GB | 11.9 GB | 10.21 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 INTELLECT 1 Instruct?
Q4_K_M · 6.8 GBINTELLECT 1 Instruct (Q4_K_M) requires 6.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 9+ GB is recommended. Using the full 8K context window can add up to 1.1 GB, bringing total usage to 7.8 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3080.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run INTELLECT 1 Instruct?
Q4_K_M · 6.8 GB58 devices with unified memory can run INTELLECT 1 Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, iPad Pro M5 13" (16 GB).
Runs great
— Plenty of headroomWhere to Download INTELLECT 1 Instruct
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Benchmarks
Benchmark details →Frequently Asked Questions
- How much VRAM does INTELLECT 1 Instruct need?
INTELLECT 1 Instruct requires 6.8 GB of VRAM at Q4_K_M, or 21.1 GB at BF16. Full 8K context adds up to 1.1 GB (7.8 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 10.2B × 4.8 bits ÷ 8 = 6.1 GB
KV Cache + Overhead ≈ 0.7 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 1.7 GB (at full 8K context)
VRAM usage by quantization
Q4_K_M6.8 GBQ4_K_M + full context7.8 GB- What's the best quantization for INTELLECT 1 Instruct?
For INTELLECT 1 Instruct, Q4_K_M (6.8 GB) offers the best balance of quality and VRAM usage. Q4_K_L (6.9 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XS at 3.7 GB.
VRAM requirement by quantization
IQ2_XS3.7 GBQ2_K5.0 GBIQ4_XS6.1 GBQ4_K_M ★6.8 GBQ5_K_S7.7 GBBF1621.1 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run INTELLECT 1 Instruct on a Mac?
INTELLECT 1 Instruct requires at least 3.7 GB at IQ2_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 INTELLECT 1 Instruct locally?
Yes — INTELLECT 1 Instruct can run locally on consumer hardware. At Q4_K_M quantization it needs 6.8 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is INTELLECT 1 Instruct?
At Q4_K_M, INTELLECT 1 Instruct can reach ~708 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~97 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 ÷ 6.8 × 0.65 = ~767 tok/s
Estimated speed at Q4_K_M (6.8 GB)
~767 tok/s~97 tok/s~767 tok/s~708 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of INTELLECT 1 Instruct?
At Q4_K_M, the download is about 6.13 GB. The full-precision BF16 version is 20.42 GB. The smallest option (IQ2_XS) is 3.06 GB.
- Which GPUs can run INTELLECT 1 Instruct?
52 consumer GPUs can run INTELLECT 1 Instruct at Q4_K_M (6.8 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 38 GPUs have plenty of headroom for comfortable inference.
- Which devices can run INTELLECT 1 Instruct?
59 devices with unified memory can run INTELLECT 1 Instruct at Q4_K_M (6.8 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.