IBM Granite Code 8B Instruct
By IBM · United States
Updated 2026-08-28
Overview
IBM Granite Code 8B Instruct is a dense, code-focused model tuned for instruction-following, with 128K context and a ~4.6GB Q4 footprint under Apache 2.0.
When to pick this model
- Instruction-driven coding tasks: explain, refactor, or generate code from prompts
- Local deployment on an 8GB GPU
- Projects requiring a permissively licensed, code-specialized assistant
- Long-context code review or documentation tasks up to 128K tokens
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 4.6 GB |
| Q5_K_M | 6 GB |
| Q8_0 | 9 GB |
| FP16 (no quantization) | 16 GB |
VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.
In practice, IBM Granite Code 8B Instruct fits an 8 GB consumer card at Q4_K_M (4.6 GB). Stepping up to Q8_0 nearly doubles the footprint to 9 GB, and unquantized FP16 weights take 16 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, IBM Granite Code 8B Instruct needs roughly 10 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 32 tokens/sec on entry-level GPUs, on the order of 50 tokens/sec on a mid-range card, and up to 75 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.
What hardware do you need
The table below matches IBM Granite Code 8B Instruct to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.
| GPU memory | Example cards | Best fit for IBM Granite Code 8B Instruct |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | Q5_K_M (6 GB used) |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | Q8_0 (9 GB used) |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | FP16 (16 GB used) |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | FP16 (16 GB used) |
| 32 GB | RTX 5090 | FP16 (16 GB used) |
Which GPU should you buy to run IBM Granite Code 8B Instruct?
To run IBM Granite Code 8B Instruct locally at Q4, you need ~4.6 GB of VRAM. The best value for this is a RTX 5060 (8 GB VRAM).
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Strengths
- Fits on an 8GB GPU at Q4 (~4.6GB)
- Code-specialized Instruct variant tuned for following directions
- Apache 2.0 license with no usage restrictions
- 128K native context window
Limitations
- Trails more recent 2025-2026 coding models
- Weights are gated on Hugging Face, requiring manual acceptance
Typical workloads
In our catalog grid, IBM Granite Code 8B Instruct is filed under Code Completion, Local Dev Assistant, 8GB GPU — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: code generation and review (pair it with an editor integration like Continue.dev or Cline).
The 125k-token context window covers long chats and mid-sized documents, though very large retrieval workloads will need chunking. The Apache 2.0 license is permissive, so shipping it inside a commercial product raises no special legal questions.
Architecture & training
Architecture: Dense transformer · 8B parameters · 128K context · code-specialized
Training: IBM's Granite Code model in its Instruct variant, built for code generation, completion, and explanation. Apache 2.0 license.
A solid instruction-tuned code model for local use, though newer releases now outperform it.
Quick start
ollama pull granite-code:8bOr use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.
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Frequently asked questions
How much VRAM does IBM Granite Code 8B Instruct need?
At the recommended Q4_K_M quantization, IBM Granite Code 8B Instruct needs about 4.6 GB of VRAM. Q8_0 takes 9 GB, and unquantized FP16 weights take 16 GB.
Can IBM Granite Code 8B Instruct run without a GPU?
Yes — with roughly 10 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.
What context window does IBM Granite Code 8B Instruct support?
IBM Granite Code 8B Instruct supports a 125k-token context window (128,000 tokens).
Can I use IBM Granite Code 8B Instruct commercially?
Yes. IBM Granite Code 8B Instruct is released under Apache 2.0, a permissive open-source license that allows commercial use, modification and redistribution.
How fast is IBM Granite Code 8B Instruct on consumer hardware?
Our compatibility engine estimates on the order of 50 tokens/sec on a mid-range GPU and up to 75 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of IBM Granite Code 8B Instruct should I download first?
Start with Q4_K_M (4.6 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. It already fits an 8 GB card at Q5_K_M.
Is IBM Granite Code 8B Instruct the right pick for you?