Granite 4.2 8B
Granite 4.2 8B is IBM's dense, Apache-2.0-licensed model built for enterprise chat, code, and reasoning workloads, with a native 128K context window and a ~4.6GB Q4 footprint.
By IBM · United States
Updated 2026-09-15
When to pick this model
- Building enterprise chatbots or internal tools that need a permissive license
- RAG pipelines requiring long-context, multilingual retrieval and synthesis
- Running a capable assistant on a single 6-8GB GPU
- Tool-calling agents that need predictable, auditable output
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, Granite 4.2 8B 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, Granite 4.2 8B 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 Granite 4.2 8B 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 4.2 8B |
|---|---|---|
| 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 hardware should you buy to run Granite 4.2 8B?
To run Granite 4.2 8B locally at Q4, you need ~4.6 GB for Q4 weights alone. Hardware option to compare: RTX 5060 Ti 16GB (ASUS Prime). Leave memory for the system and context; verify inference-engine support. A mini PC does not provide CUDA or macOS/MLX.
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Strengths
- Fits on 6-8GB GPUs at Q4 quantization
- 128K native context window
- Apache 2.0 license with no usage restrictions
- Solid multilingual and enterprise tool-calling support
Limitations
- Weights are gated on Hugging Face, requiring manual acceptance
- 8B scale trails larger models on complex reasoning and coding tasks
Typical workloads
In our catalog grid, Granite 4.2 8B is filed under Enterprise Assistant, Multilingual RAG, Code (8B Class) — 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); multi-step reasoning and math-flavoured tasks; multilingual workloads.
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
Training: IBM's Granite 4.2 model, built for enterprise use (chat, code, reasoning, multilingual). Apache 2.0 license.
A dependable, low-VRAM enterprise workhorse for chat, RAG, and tool-calling under a fully permissive license.
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.
ollama pull granite4.2Or 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 4.2 8B need?
At the recommended Q4_K_M quantization, Granite 4.2 8B needs about 4.6 GB of VRAM. Q8_0 takes 9 GB, and unquantized FP16 weights take 16 GB.
Can Granite 4.2 8B 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 Granite 4.2 8B support?
Granite 4.2 8B supports a 125k-token context window (128,000 tokens).
Can I use Granite 4.2 8B commercially?
Yes. Granite 4.2 8B is released under Apache 2.0, a permissive open-source license that allows commercial use, modification and redistribution.
How fast is Granite 4.2 8B 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 Granite 4.2 8B 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.