OCC RAG 1.7B — Hardware Requirements & GPU Compatibility
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OCC RAG 1.7B is a 1.7B-parameter open language model from occ-ai. It supports a context window of up to 32,768 tokens. At Q4_K_M it needs about 1.57 GB of VRAM — see which GPUs and Macs can run it below.
613 downloads
4 likesSpecifications
- Publisher
- occ-ai
- Parameters
- 1.7B
- Architecture
- Qwen3ForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2026-05-29
- License
- MIT
Get Started
HuggingFace
How Much VRAM Does OCC RAG 1.7B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 1.3 GB | 4.8 GB | 0.73 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 1.4 GB | 4.9 GB | 0.84 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 1.6 GB | 5.1 GB | 1.03 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 1.8 GB | 5.3 GB | 1.23 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 1.9 GB | 5.5 GB | 1.42 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 2.3 GB | 5.8 GB | 1.72 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 4.0 GB | 7.5 GB | 3.44 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 OCC RAG 1.7B?
Q4_K_M · 1.6 GBOCC RAG 1.7B (Q4_K_M) requires 1.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 3+ GB is recommended. Using the full 33K context window can add up to 3.5 GB, bringing total usage to 5.1 GB. 50 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run OCC RAG 1.7B?
Q4_K_M · 1.6 GB59 devices with unified memory can run OCC RAG 1.7B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does OCC RAG 1.7B need?
OCC RAG 1.7B requires 1.6 GB of VRAM at Q4_K_M, or 4.0 GB at BF16. Full 33K context adds up to 3.5 GB (5.1 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 1.7B × 4.8 bits ÷ 8 = 1 GB
KV Cache + Overhead ≈ 0.6 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 4.1 GB (at full 33K context)
VRAM usage by quantization
Q4_K_M1.6 GBQ4_K_M + full context5.1 GB- What's the best quantization for OCC RAG 1.7B?
For OCC RAG 1.7B, Q4_K_M (1.6 GB) offers the best balance of quality and VRAM usage. Q5_K_M (1.8 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 1.3 GB.
VRAM requirement by quantization
Q2_K1.3 GBQ4_K_M ★1.6 GBQ5_K_M1.8 GBQ6_K1.9 GBQ8_02.3 GBBF164.0 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run OCC RAG 1.7B on a Mac?
OCC RAG 1.7B requires at least 1.3 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 OCC RAG 1.7B locally?
Yes — OCC RAG 1.7B can run locally on consumer hardware. At Q4_K_M quantization it needs 1.6 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is OCC RAG 1.7B?
At Q4_K_M, OCC RAG 1.7B can reach ~2803 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~417 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 ÷ 1.6 × 0.65 = ~3312 tok/s
Estimated speed at Q4_K_M (1.6 GB)
~3312 tok/s~417 tok/s~3312 tok/s~2803 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of OCC RAG 1.7B?
At Q4_K_M, the download is about 1.03 GB. The full-precision BF16 version is 3.44 GB. The smallest option (Q2_K) is 0.73 GB.
- Which GPUs can run OCC RAG 1.7B?
50 consumer GPUs can run OCC RAG 1.7B at Q4_K_M (1.6 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 50 GPUs have plenty of headroom for comfortable inference.
- Which devices can run OCC RAG 1.7B?
59 devices with unified memory can run OCC RAG 1.7B at Q4_K_M (1.6 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.