OmniCoder 9B — Hardware Requirements & GPU Compatibility
ChatCodeFunctions
5.7K downloads
199 likesSpecifications
- Publisher
- Tesslate
- Parameters
- 9B
- Architecture
- Qwen3_5ForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 248,320
- Release Date
- 2026-03-13
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does OmniCoder 9B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 4.4 GB | 38.5 GB | 3.83 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 4.5 GB | 38.6 GB | 3.94 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 5.0 GB | 39.0 GB | 4.39 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 5.1 GB | 39.2 GB | 4.50 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 6.0 GB | 40.1 GB | 5.40 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 7.0 GB | 41.1 GB | 6.41 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 8.0 GB | 42.1 GB | 7.42 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 9.6 GB | 43.7 GB | 9.00 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run OmniCoder 9B?
Q4_K_M · 6.0 GBOmniCoder 9B (Q4_K_M) requires 6.0 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 8+ GB is recommended. Using the full 262K context window can add up to 34.1 GB, bringing total usage to 40.1 GB. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti.
Runs great
— Plenty of headroom
Which Devices Can Run OmniCoder 9B?
Q4_K_M · 6.0 GB33 devices with unified memory can run OmniCoder 9B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Air 13" M3 (8 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightRelated Models
Derivatives (2)
Frequently Asked Questions
- How much VRAM does OmniCoder 9B need?
OmniCoder 9B requires 6.0 GB of VRAM at Q4_K_M, or 9.6 GB at Q8_0. Full 262K context adds up to 34.1 GB (40.1 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 9B × 4.8 bits ÷ 8 = 5.4 GB
KV Cache + Overhead ≈ 0.6 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 34.7 GB (at full 262K context)
VRAM usage by quantization
Q4_K_M6.0 GBQ4_K_M + full context40.1 GB- What's the best quantization for OmniCoder 9B?
For OmniCoder 9B, Q4_K_M (6.0 GB) offers the best balance of quality and VRAM usage. Q4_K_L (6.1 GB) provides better quality if you have the VRAM. The smallest option is IQ2_S at 3.4 GB.
VRAM requirement by quantization
IQ2_S3.4 GB~59%Q3_K_S4.5 GB~77%IQ4_XS5.4 GB~87%Q4_K_M ★6.0 GB~89%Q4_K_L6.1 GB~90%Q8_09.6 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run OmniCoder 9B on a Mac?
OmniCoder 9B requires at least 3.4 GB at IQ2_S, 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 OmniCoder 9B locally?
Yes — OmniCoder 9B can run locally on consumer hardware. At Q4_K_M quantization it needs 6.0 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is OmniCoder 9B?
At Q4_K_M, OmniCoder 9B can reach ~488 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~110 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 6.0 × 0.55 = ~488 tok/s
Estimated speed at Q4_K_M (6.0 GB)
AMD Instinct MI300X~488 tok/sNVIDIA GeForce RTX 4090~110 tok/sNVIDIA H100 SXM~365 tok/sAMD Instinct MI250X~302 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of OmniCoder 9B?
At Q4_K_M, the download is about 5.40 GB. The full-precision Q8_0 version is 9.00 GB. The smallest option (IQ2_S) is 2.81 GB.
- Which GPUs can run OmniCoder 9B?
35 consumer GPUs can run OmniCoder 9B at Q4_K_M (6.0 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 28 GPUs have plenty of headroom for comfortable inference.
- Which devices can run OmniCoder 9B?
33 devices with unified memory can run OmniCoder 9B at Q4_K_M (6.0 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.