Intern Decision 4B — Hardware Requirements & GPU Compatibility
Vision
Intern-Decision-4B is InternLM's 4.5-billion-parameter multimodal structured decision model, fine-tuned from Qwen3.5-4B. It accepts a shared state, a schema of named questions and optional images, and returns an answer distribution for every question in one forward pass by scoring candidate option symbols instead of sampling text. The card reports an average of 90.02 across its decision benchmarks, the highest in its family table, with roughly 44 ms per query on a single RTX 4090. At this size it fits on a single consumer GPU, including mid-range cards. The model supports a 262,144 token context window. It is released under the Apache 2.0 license, permitting unrestricted commercial and research use, and was published in September 2026. It is the largest of the Intern-Decision models listed in the card, alongside 0.8B and 2B siblings, and it depends on the custom inference code in the repository rather than a standard chat workflow.
1.6K downloads
75 likesSpecifications
- Publisher
- InternLM
- Parameters
- 4.5B
- Architecture
- Qwen3_5ForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 248,320
- Release Date
- 2026-09-26
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Intern Decision 4B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 2.4 GB | 23.7 GB | 1.93 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 2.7 GB | 24.0 GB | 2.21 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 3.2 GB | 24.5 GB | 2.72 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 3.7 GB | 25.0 GB | 3.23 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 4.2 GB | 25.5 GB | 3.74 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 5.0 GB | 26.3 GB | 4.54 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 9.6 GB | 30.9 GB | 9.08 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 Intern Decision 4B?
Q4_K_M · 3.2 GBIntern Decision 4B (Q4_K_M) requires 3.2 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 5+ GB is recommended. Using the full 262K context window can add up to 21.3 GB, bringing total usage to 24.5 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Intern Decision 4B?
Q4_K_M · 3.2 GB59 devices with unified memory can run Intern Decision 4B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomWhere to Download Intern Decision 4B
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
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Frequently Asked Questions
- How much VRAM does Intern Decision 4B need?
Intern Decision 4B requires 3.2 GB of VRAM at Q4_K_M, or 9.6 GB at BF16. Full 262K context adds up to 21.3 GB (24.5 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 4.5B × 4.8 bits ÷ 8 = 2.7 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 21.8 GB (at full 262K context)
VRAM usage by quantization
Q4_K_M3.2 GBQ4_K_M + full context24.5 GB- What's the best quantization for Intern Decision 4B?
For Intern Decision 4B, Q4_K_M (3.2 GB) offers the best balance of quality and VRAM usage. Q5_K_M (3.7 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 2.4 GB.
VRAM requirement by quantization
Q2_K2.4 GBQ4_K_M ★3.2 GBQ5_K_M3.7 GBQ6_K4.2 GBQ8_05.0 GBBF169.6 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Intern Decision 4B on a Mac?
Intern Decision 4B requires at least 2.4 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 Intern Decision 4B locally?
Yes — Intern Decision 4B can run locally on consumer hardware. At Q4_K_M quantization it needs 3.2 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Intern Decision 4B?
At Q4_K_M, Intern Decision 4B can reach ~1505 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~205 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 ÷ 3.2 × 0.65 = ~1630 tok/s
Estimated speed at Q4_K_M (3.2 GB)
~1630 tok/s~205 tok/s~1630 tok/s~1505 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Intern Decision 4B?
At Q4_K_M, the download is about 2.72 GB. The full-precision BF16 version is 9.08 GB. The smallest option (Q2_K) is 1.93 GB.
- Which GPUs can run Intern Decision 4B?
52 consumer GPUs can run Intern Decision 4B at Q4_K_M (3.2 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 Intern Decision 4B?
59 devices with unified memory can run Intern Decision 4B at Q4_K_M (3.2 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.