Zenith 9B CodeCore Merge — Hardware Requirements & GPU Compatibility
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Zenith 9B CodeCore Merge is a 9.7B-parameter open language model from prithivMLmods. It supports a context window of up to 262,144 tokens. At Q4_K_M it needs about 6.36 GB of VRAM — see which GPUs and Macs can run it below.
504 downloads
3 likesSpecifications
- Publisher
- prithivMLmods
- Parameters
- 9.7B
- Architecture
- Qwen3_5ForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 248,320
- Release Date
- 2026-09-15
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Zenith 9B CodeCore Merge Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 4.7 GB | 38.8 GB | 4.10 GB | 2-bit quantization with K-quant improvements |
| Q3_K_M | 3.90 | 5.3 GB | 39.4 GB | 4.71 GB | 3-bit medium quantization |
| Q3_K_L | 4.10 | 5.5 GB | 39.6 GB | 4.95 GB | 3-bit large quantization |
| Q4_K_S | 4.50 | 6 GB | 40.1 GB | 5.43 GB | 4-bit small quantization |
| Q4_K_M | 4.80 | 6.4 GB | 40.5 GB | 5.79 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_S | 5.50 | 7.2 GB | 41.3 GB | 6.64 GB | 5-bit small quantization |
| Q5_K_M | 5.70 | 7.5 GB | 41.5 GB | 6.88 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 8.5 GB | 42.6 GB | 7.96 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 10.2 GB | 44.3 GB | 9.65 GB | 8-bit quantization, near-lossless |
| BF16 | 16.00 | 19.9 GB | 54.0 GB | 19.31 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 Zenith 9B CodeCore Merge?
Q4_K_M · 6.4 GBZenith 9B CodeCore Merge (Q4_K_M) requires 6.4 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 262K context window can add up to 34.1 GB, bringing total usage to 40.5 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Zenith 9B CodeCore Merge?
Q4_K_M · 6.4 GB58 devices with unified memory can run Zenith 9B CodeCore Merge, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Air 13" M3 (8 GB).
Runs great
— Plenty of headroomWhere to Download Zenith 9B CodeCore Merge
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Related Models
Frequently Asked Questions
- How much VRAM does Zenith 9B CodeCore Merge need?
Zenith 9B CodeCore Merge requires 6.4 GB of VRAM at Q4_K_M, or 19.9 GB at BF16. Full 262K context adds up to 34.1 GB (40.5 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 9.7B × 4.8 bits ÷ 8 = 5.8 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_M6.4 GBQ4_K_M + full context40.5 GB- What's the best quantization for Zenith 9B CodeCore Merge?
For Zenith 9B CodeCore Merge, Q4_K_M (6.4 GB) offers the best balance of quality and VRAM usage. Q5_K_S (7.2 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 4.7 GB.
VRAM requirement by quantization
Q2_K4.7 GBQ3_K_L5.5 GBQ4_K_M ★6.4 GBQ5_K_S7.2 GBQ6_K8.5 GBBF1619.9 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Zenith 9B CodeCore Merge on a Mac?
Zenith 9B CodeCore Merge requires at least 4.7 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 Zenith 9B CodeCore Merge locally?
Yes — Zenith 9B CodeCore Merge can run locally on consumer hardware. At Q4_K_M quantization it needs 6.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Zenith 9B CodeCore Merge?
At Q4_K_M, Zenith 9B CodeCore Merge can reach ~755 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~103 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 ÷ 6.4 × 0.65 = ~818 tok/s
Estimated speed at Q4_K_M (6.4 GB)
~818 tok/s~103 tok/s~818 tok/s~755 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Zenith 9B CodeCore Merge?
At Q4_K_M, the download is about 5.79 GB. The full-precision BF16 version is 19.31 GB. The smallest option (Q2_K) is 4.10 GB.
- Which GPUs can run Zenith 9B CodeCore Merge?
52 consumer GPUs can run Zenith 9B CodeCore Merge at Q4_K_M (6.4 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 40 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Zenith 9B CodeCore Merge?
59 devices with unified memory can run Zenith 9B CodeCore Merge at Q4_K_M (6.4 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.