Granite Code 20B (Mixed Precision)

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

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.

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 for Q4 weights alone. Hardware option to compare: RTX 5070 12GB (ASUS Prime OC). Leave memory for the system and context; verify inference-engine support. A mini PC does not provide CUDA or macOS/MLX.

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.

Quick start

Install the runtime for your system: Windows, macOS or Linux. Check the exact model tag or GGUF quantization below; catalog IDs are not necessarily Ollama tags.

Start at 4096 tokens of context, then use ollama ps to check GPU/CPU placement. A default download may use a different quantization from the configurator’s memory estimate. Keep the free setup working before considering a kit.

# HuggingFace : ibm-granite/granite-code-20b-mixed-precision

Or 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 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.