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.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 32K)Best on 24 GB (Q4_K_M · ~103 tok/s · 256K)Macs from 16 GB (Q4_K_M · ~22 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Zenith 9B CodeCore Merge
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
ollama run hf.co/prithivMLmods/Zenith-9B-CodeCore-Merge-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 17.4 GB free · 16K context
Download: Zenith-9B-CodeCore-Merge.Q4_K_M.gguf5.63 GBprithivMLmods
Ways to runOther ways
each opens step-by-stepRun in cloud
Your own private copy · Q4_K_M · 24 GB GPU
- The easiest way to try this model
- Running in a few minutes, easy setup
- Chat right here or connect any app
$5+ bonus for new users
Runs on RTX 3080 Ti (12 GB) (5 GB headroom) · Q4_K_M
- Generation speed
- ~93 tok/s
- generation speed
- Cost per 1M output tokens
- $0.36
- per 1M output tokens
How Much VRAM Does Zenith 9B CodeCore Merge Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 4.7 GB | 5.5 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 256K context · ~140 tok/s | |
| 5.3 GB | 6.1 GB | 3-bit medium — 3-bit medium quantization | Fits: 256K context · ~124 tok/s | |
| 5.5 GB | 6.3 GB | 3-bit large — 3-bit large quantization | Fits: 256K context · ~119 tok/s | |
| 6 GB | 6.8 GB | 4-bit small — 4-bit small quantization | Fits: 256K context · ~109 tok/s | |
| 6.4 GB | 7.2 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 256K context · ~103 tok/s | |
| 7.2 GB | 8 GB | 5-bit small — 5-bit small quantization | Fits: 256K context · ~91 tok/s | |
| 7.5 GB | 8.3 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 256K context · ~88 tok/s | |
| 8.5 GB | 9.3 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 256K context · ~77 tok/s | |
| 10.2 GB | 11 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 256K context · ~64 tok/s | |
| 19.9 GB | 20.7 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 128K context · ~33 tok/s |
Selected Q4_K_M
4-bit medium quantization6.6GBat 16K context
- Weights
- 5.8 GB
- KV cache
- 0.5 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 17.4 GB free · ~103 tok/s
≥50 tok/s needs ≥489 GB/s · RTX 5090 ~183, RTX 3090 Ti ~103
Which Hardware 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 8.3 GB, bringing total usage to 14.7 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti. 151 devices with unified memory can run Zenith 9B CodeCore Merge, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), NVIDIA Jetson Orin Nano 8GB (Super).
≤ 11 GB
≤ 11 GB tier →fits · 1.4 GB free · up to 32K ctx
16 GPUs · 1 device family
RTX 3080~78Simulate output on RTX 3080RTX 2080 Ti~63Simulate output on RTX 2080 TiRTX 3070 Ti~62Simulate output on RTX 3070 TiOrin Nano~10Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)20 GB
fits · 13.4 GB free · up to 256K ctx
1 GPU
RX 7900 XT~76Simulate output on RX 7900 XT32–40 GB
fits · 25.4 GB free · up to 256K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~183Simulate output on RTX 5090M5 Max36 GB~51Simulate output on Mac Studio M5 Max (36 GB)M532 GB~17Simulate output on MacBook Air 13" M5 (32 GB)Orin~21Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~14Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Qwen3.5 9BAlibaba | 99.4 | 6.6 GB | ~103 | same size |
| Gemma 2 9B ITGoogle | 68.0 | 7.3 GB | ~107 | same size |
| Ornith 1.5 9Bornith-ai | — | 6.6 GB | ~103 | same size |
| MiniCPM O 4 5OpenBMB | — | 8.3 GB | ~105 | same size |
| GLM 4.1V 9B ThinkingZ.ai | — | 7.2 GB | ~100 | same size |
| Shieldgemma 9BGoogle | — | 8.8 GB | ~107 | same size |
Specifications
- 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
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 8.3 GB (14.7 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)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 8.9 GB (at full 262K context)
VRAM usage by quantization
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
★ Recommended — best balance of quality and VRAM usage.
Can I run Zenith 9B CodeCore Merge on a Mac?
Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run Zenith 9B CodeCore Merge. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 6.4 GB of usable unified memory (RAM minus macOS overhead).
Can I run Zenith 9B CodeCore Merge locally?
Yes — Zenith 9B CodeCore Merge can run locally on consumer hardware. 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 ÷ 6.36 × 0.65 = ~818 tok/s
Estimated speed at Q4_K_M (6.4 GB)
Real-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. 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?
151 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, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (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.