Granite 34B Code Instruct 8k GGUF — Hardware Requirements & GPU Compatibility
ChatCode
40 downloads
6 likesSpecifications
- Publisher
- IBM
- Parameters
- 34B
- Release Date
- 2024-09-02
- License
- Apache 2.0
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How Much VRAM Does Granite 34B Code Instruct 8k GGUF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q4_K_M | 4.80 | 22.4 GB | — | 20.40 GB | 4-bit medium quantization — most popular sweet spot |
Which GPUs Can Run Granite 34B Code Instruct 8k GGUF?
Q4_K_M · 22.4 GBGranite 34B Code Instruct 8k GGUF (Q4_K_M) requires 22.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 30+ GB is recommended. 5 GPUs can run it, including NVIDIA GeForce RTX 5090.
All compatible consumer-level GPUs are running near their VRAM limit. You may also want to consider professional GPUs (e.g., NVIDIA A100, H100) which offer significantly more VRAM. For more headroom and better throughput, consider a multi-GPU configuration with tensor parallelism (supported by tools like vLLM, llama.cpp, or text-generation-inference).
Which Devices Can Run Granite 34B Code Instruct 8k GGUF?
Q4_K_M · 22.4 GB21 devices with unified memory can run Granite 34B Code Instruct 8k GGUF, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does Granite 34B Code Instruct 8k GGUF need?
Granite 34B Code Instruct 8k GGUF requires 22.4 GB of VRAM at Q4_K_M.
VRAM = Weights + KV Cache + Overhead
Weights = 34B × 4.8 bits ÷ 8 = 20.4 GB
KV Cache + Overhead ≈ 2 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M22.4 GB- Can I run Granite 34B Code Instruct 8k GGUF on a Mac?
Granite 34B Code Instruct 8k GGUF requires at least 22.4 GB at Q4_K_M, 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 Granite 34B Code Instruct 8k GGUF locally?
Yes — Granite 34B Code Instruct 8k GGUF can run locally on consumer hardware. At Q4_K_M quantization it needs 22.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Granite 34B Code Instruct 8k GGUF?
At Q4_K_M, Granite 34B Code Instruct 8k GGUF can reach ~130 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~29 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 ÷ 22.4 × 0.55 = ~130 tok/s
Estimated speed at Q4_K_M (22.4 GB)
AMD Instinct MI300X~130 tok/sNVIDIA GeForce RTX 4090~29 tok/sNVIDIA H100 SXM~97 tok/sAMD Instinct MI250X~80 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Granite 34B Code Instruct 8k GGUF?
At Q4_K_M, the download is about 20.40 GB.
- Which GPUs can run Granite 34B Code Instruct 8k GGUF?
5 consumer GPUs can run Granite 34B Code Instruct 8k GGUF at Q4_K_M (22.4 GB). Top options include AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090.
- Which devices can run Granite 34B Code Instruct 8k GGUF?
21 devices with unified memory can run Granite 34B Code Instruct 8k GGUF at Q4_K_M (22.4 GB), including Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB), Mac Pro M2 Ultra (192 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.