Granite Code 20B (Mixed Precision)
By IBM · United States
Updated 2026-08-31
Overview
IBM's dense 20B Granite Code model in a mixed-precision checkpoint, purpose-built for code generation and completion. Native 128K context, ~12GB VRAM at Q4, Apache 2.0.
When to pick this model
- Local code completion and generation on a single 12GB+ GPU
- Projects needing a fully open, commercially permissive code model
- Workloads spanning very long files or multi-file context up to 128K tokens
- Teams standardizing on IBM's Granite model family
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 12 GB |
| Q5_K_M | 14 GB |
| Q8_0 | 21 GB |
| FP16 (no quantization) | 40 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, Granite Code 20B (Mixed Precision) needs a 12 GB card at Q4_K_M (12 GB). Stepping up to Q8_0 nearly doubles the footprint to 21 GB, and unquantized FP16 weights take 40 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, Granite Code 20B (Mixed Precision) needs roughly 26 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 9 tokens/sec on entry-level GPUs, on the order of 14 tokens/sec on a mid-range card, and up to 22 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.
What hardware do you need
The table below matches Granite Code 20B (Mixed Precision) 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 Granite Code 20B (Mixed Precision) |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | Does not fit — needs 12 GB at Q4_K_M |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | Q4_K_M (12 GB used) |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | Q5_K_M (14 GB used) |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | Q8_0 (21 GB used) |
| 32 GB | RTX 5090 | Q8_0 (21 GB used) |
Which hardware should you buy to run Granite Code 20B (Mixed Precision)?
To run Granite Code 20B (Mixed Precision) locally at Q4, you need ~12 GB of VRAM. The best value for this today is a RTX 5070 12GB (ASUS TUF Gaming OC) (12 GB VRAM).
As an Amazon Associate, BestLLMfor earns from qualifying purchases, at no extra cost to you. It does not influence our independent rankings.
Strengths
- Apache 2.0 license with no commercial restrictions
- 20B dense parameters focused specifically on code
- 128K native context window
- Clear code specialization rather than a general-purpose compromise
Limitations
- Gated on Hugging Face, requiring click-through access
- No official Ollama tag — install via Hugging Face directly
- Mixed-precision checkpoint means runtime compatibility needs verification
Typical workloads
In our catalog grid, Granite Code 20B (Mixed Precision) is filed under Code Generation, IDE Completion, Dev Assistant — 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 20B · code-specialized · mixed-precision checkpoint
Training: IBM Granite Code: an open-source model family dedicated to code, trained on 116 programming languages. This mixed-precision variant optimizes the memory footprint at inference time.
A focused, Apache-licensed 20B code model for teams that want strong completion without a general-purpose model's overhead.
Quick start
# HuggingFace : ibm-granite/granite-code-20b-mixed-precisionOr use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.
Similar models worth comparing
Frequently asked questions
How much VRAM does Granite Code 20B (Mixed Precision) need?
At the recommended Q4_K_M quantization, Granite Code 20B (Mixed Precision) needs about 12 GB of VRAM. Q8_0 takes 21 GB, and unquantized FP16 weights take 40 GB.
Can Granite Code 20B (Mixed Precision) run without a GPU?
Yes — with roughly 26 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 Granite Code 20B (Mixed Precision) support?
Granite Code 20B (Mixed Precision) supports a 125k-token context window (128,000 tokens).
Can I use Granite Code 20B (Mixed Precision) commercially?
Yes. Granite Code 20B (Mixed Precision) is released under Apache 2.0, a permissive open-source license that allows commercial use, modification and redistribution.
How fast is Granite Code 20B (Mixed Precision) on consumer hardware?
Our compatibility engine estimates on the order of 14 tokens/sec on a mid-range GPU and up to 22 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of Granite Code 20B (Mixed Precision) should I download first?
Start with Q4_K_M (12 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. On a 24 GB card you can run up to Q8_0.
Is Granite Code 20B (Mixed Precision) the right pick for you?