Granite 4.1 8B Instruct

Overview

IBM's dense 8B Granite 4.1 release: Apache 2.0, 12 languages, 131k context, MMLU 73.84, HumanEval 85.37. Trained on a CoreWeave GB200 NVL72 cluster.

When to pick this model

• Enterprise deployments needing Apache 2.0 and IBM provenance
• Tool-calling agents with predictable behavior at moderate scale
• Multilingual products across 12 languages including French
• Long-context tasks up to 131k tokens
• Coding workloads on a single mid-range GPU

VRAM requirements by quantization

Quantization	VRAM required
Q4_K_M (recommended)	5 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 4.1 8B Instruct fits an 8 GB consumer card at Q4_K_M (5 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 4.1 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 12 tokens/sec on entry-level GPUs, on the order of 35 tokens/sec on a mid-range card, and up to 90 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.

What hardware do you need

The table below matches Granite 4.1 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 Granite 4.1 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 Granite 4.1 8B Instruct?

To run Granite 4.1 8B Instruct locally at Q4, you need ~5 GB of VRAM. The best value for this is a RTX 5060 (8 GB VRAM).

Published benchmark scores

Benchmark	Score
MMLU	73.84
GSM8K	92.49
HumanEval	85.37
ArenaHard	68.98
AlpacaEval	50.08

Scores published by the model author or aggregated from public leaderboards. Re-measured monthly by our editorial team.

To put Granite 4.1 8B Instruct in context: its MMLU score of 73.84 ranks #16 of the 34 catalog models with a published MMLU result (catalog median 73.4); its GSM8K score of 92.49 ranks #3 of the 9 catalog models with a published GSM8K result (catalog median 83.1); its HumanEval score of 85.37 ranks #8 of the 26 catalog models with a published HumanEval result (catalog median 81.1). Scores are author-published and measured under different harnesses, so treat gaps of a few points as noise rather than a verdict.

Strengths

• Apache 2.0 with full transparency on training
• Strong tool calling and instruction following
• 12 native languages including French
• 131k context window
• Excellent quality-per-parameter at the 8B tier

Limitations

• No official Ollama tag at release
• Reasoning in non-English languages still trails English
• No MoE variant at this size

Typical workloads

In our catalog grid, Granite 4.1 8B Instruct is filed under Enterprise Tool Calling, Multilingual RAG, 8B Code 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); multilingual workloads.

The 128k-token context window is large enough to hold entire codebases' worth of files or long reports in a single prompt, which is what makes local RAG and document analysis practical. The Apache 2.0 license is permissive, so shipping it inside a commercial product raises no special legal questions.

Architecture & training

Architecture: Dense Transformer · 40 layers · GQA 32Q/8KV · embedding 4096 · MLP hidden 12,800 · RoPE

Training: Improved post-training: SFT + RL alignment. 12 languages: EN, DE, ES, FR, JA, PT, AR, CS, IT, KO, NL, ZH. NVIDIA GB200 NVL72 cluster (CoreWeave).

Quick start

# HuggingFace : ibm-granite/granite-4.1-8b

Or 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 4.1 8B Instruct need?

At the recommended Q4_K_M quantization, Granite 4.1 8B Instruct needs about 5 GB of VRAM. Q8_0 takes 9 GB, and unquantized FP16 weights take 16 GB.

Can Granite 4.1 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 Granite 4.1 8B Instruct support?

Granite 4.1 8B Instruct supports a 128k-token context window (131,072 tokens).

Can I use Granite 4.1 8B Instruct commercially?

Yes. Granite 4.1 8B Instruct is released under Apache 2.0, a permissive open-source license that allows commercial use, modification and redistribution.

How fast is Granite 4.1 8B Instruct on consumer hardware?

Our compatibility engine estimates on the order of 35 tokens/sec on a mid-range GPU and up to 90 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.

Which quantization of Granite 4.1 8B Instruct should I download first?

Start with Q4_K_M (5 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.