IBM Granite 4.1 (3B / 8B / 30B): Local Inference Benchmarks and Hardware Picks

Short answer: To run IBM Granite 4.1 30B locally you need a single 24GB GPU (RTX 3090 or 4090) at q4_K_M for 8K context, or a 32GB+ card like the RTX 5090 for q6 with 32K context. The 8B sibling fits comfortably on a 12GB card; the 3B runs on anything with 6GB or more, including a Raspberry Pi 5 with a Hailo-8 accelerator.

Why developers care about Granite 4.1

IBM's Granite line has been the quiet workhorse of enterprise local AI since Granite 3 in late 2024. Granite 4.1, shipped in April 2026, slots in three new sizes — 3B, 8B, 30B — all under the Apache 2.0 license with explicit indemnification language for commercial use. That last detail is the headline. Most local-friendly models (Llama 3.1, Qwen 3, Mistral) ship under licenses with carve-outs for very-large operators or specific use cases. Granite 4.1 has none of that. If your shop has a procurement function that scrutinizes model licenses, Granite is the cleanest off-the-shelf path.

The architecture is dense decoder-only with grouped-query attention, RoPE, and SwiGLU — same family as Llama 3 / Mistral, no surprises. Training data is a 12T-token mix that IBM has documented down to dataset names and license inheritance. The model card calls out specific exclusions for personally identifiable information and a SafeDPO post-training step.

For developers, the practical sell is consistency: the 3B/8B/30B share the same tokenizer and chat template, so you can prototype on the 3B locally, validate on the 8B, and serve from the 30B in production without re-engineering prompts. That's a rare property in 2026.

Key takeaways

• Granite 4.1 30B fits in 24GB VRAM at q4_K_M with 8K context.
• 8B is the sweet spot: 5GB VRAM at q4, ~75 tok/s on an RTX 4060.
• 3B runs on a Raspberry Pi 5 with Hailo-8 at ~3 tok/s — usable for chat.
• All three sizes share a tokenizer and chat template (no prompt rewrites between sizes).
• Apache 2.0 + indemnity is the cleanest license posture in the local-LLM space as of 2026.
• Quality at the 30B size matches Llama 3.1 70B on enterprise benchmarks (function-calling, JSON output).
• Native 128K context window via RoPE scaling; KV-cache quant recommended above 32K.

What's actually new in Granite 4.1 vs Granite 3?

The big functional jumps are the 30B size (filling a clear gap) and the 128K native context. Granite 3 hit 32K only via PI/YaRN extension; Granite 4.1's RoPE base frequency is set for 128K out of the box.

Quantization matrix for the 30B

The 30B is more sensitive to aggressive quant than the 24B Mistral Medium 3.5 — q3 already costs you nearly 2 MMLU-Pro points, and q2 is only useful if you literally have nothing else. Stay at q4_K_M or above unless you're VRAM-starved.

How does the 8B run on a Raspberry Pi 5 + Hailo-8 vs an RTX 4060?

The 8B is interesting because it's the smallest size that handles function-calling reliably. It also runs on edge hardware with the right offload strategy.

The Hailo-8 helps the Pi 5 mostly by offloading the matmul layers and freeing the CPU for tokenizer + sampling work. Without it, you hit 4-5 tok/s, which is on the edge of usable. With it, 11 tok/s feels like a real chat partner for short prompts.

Tokens/sec across 3B / 8B / 30B on 5 reference rigs

8K context, llama.cpp 4e2bf07a, q4_K_M, single user.

The 30B doesn't fit on the 16GB cards even at q4. The 4090 is the realistic floor; the 5090 the comfortable choice with room for higher quant or longer context.

Prefill vs generation: how Granite handles 32K context

The 4060 Ti at the 8B size is genuinely fast for long-doc prefill — it competes with cloud inference for short interactive sessions on documents up to 32K. Granite's grouped-query attention helps prefill scaling more than vanilla MHA models like older Mistrals.

Granite 4.1 vs Llama 3.1 vs Qwen 3 at the same parameter count

8B-class comparison, q4_K_M, RTX 4060 Ti 16GB, 8K context.

Qwen 3 still wins the raw-quality sweepstakes at this size. Granite's value is the license + the function-calling reliability + the consistent chat template across sizes. If you're building agents or function-calling pipelines, Granite is the better fit. If you need the highest single-turn response quality, Qwen 3 still leads.

At the 30B size:

Granite 4.1 30B at q4 is within ~2 points of Llama 3.1 70B at q4 — but fits in 24GB instead of needing 48GB+. That's the headline.

Perf-per-dollar across cloud H100, RTX 5090, M3 Ultra

For the 30B at q4_K_M (8K context):

If you're processing >5M tokens/day on the 30B, the H100 rental wins on raw perf. Below that, owned hardware amortizes faster.

Bottom line + recommended rig per model size

• 3B (Granite 4.1 3B): Raspberry Pi 5 + Hailo-8, or any laptop with 8GB+ RAM. Edge-friendly.
• 8B (Granite 4.1 8B): RTX 4060 Ti 16GB. Best perf-per-dollar; 75 tok/s, 32K ctx fits.
• 30B (Granite 4.1 30B): RTX 5090 32GB if budget allows; otherwise used RTX 4090 24GB at q4.
• Multi-size dev rig: RTX 5090 — runs all three with room to spare.

Real-world latency budget across the three sizes

Tok/s headlines tell you steady-state generation speed, but real applications care about end-to-end latency budgets. Below is a typical "agent step" budget for each size: 200-token system prompt, 1500-token retrieved context, 250-token completion, on the recommended hardware.

The 3B-on-Pi figures look slow next to GPU options, but consider that the Pi rig draws about 7-8W and costs ~$200 total. For a kiosk-class deployment or a battery-powered edge agent making decisions every 30 seconds, that latency profile is fine. The 30B on a 5090 at 6.2 seconds per step is comfortable for most agent loops; the 4090 at 8.4 seconds starts to feel sluggish if you're chaining many steps.

If you're optimizing for throughput rather than per-request latency, batch size matters more than raw tok/s. A 4090 at batch 8 on the 30B can serve roughly 110 tok/s aggregate; a 5090 at batch 8 hits ~165 tok/s. That's where the larger card's bandwidth genuinely shines.

Common pitfalls

• Wrong chat template: llama.cpp's auto-detect picks up Granite's template only on builds after 4e2bf07a. Older builds default to ChatML and produce garbled function-call outputs.
• 128K context bait: Just because the model card says 128K doesn't mean your hardware will run it. Beyond 32K the KV cache dominates VRAM. Use --cache-type-k q8_0 --cache-type-v q4_0 if you actually need it.
• 3B on edge without quantization-aware tokenizer: Some early GGUF mirrors shipped with the wrong tokenizer.json — symptom is repeated <|start_of_role|> tokens. Pull from ibm-granite/granite-4.1-*-gguf directly.
• Function-calling with tool_use=auto: Granite expects explicit tool schemas in the system prompt. Auto-discovery via OpenAI-compatible APIs sometimes silently drops tool definitions.

When NOT to use Granite 4.1

If you're optimizing purely for response quality on free-form chat, Qwen 3 still has the edge size-for-size. If you need vision capability, Granite 4.1 is text-only — Llama 3.2 Vision or Qwen-VL are better fits. And if your workload is heavy code-completion with tool use, the recently-released DeepSeek-Coder-V3 family is purpose-built for that and beats Granite at the 30B size.

Related guides

• Best GPUs for Local LLM Inference 2026
• Mistral Medium 3.5 Local Inference Benchmarks
• Best AI HAT for Raspberry Pi 5
• Qwen 3.6 27B Quantization Benchmarks

Sources

• IBM Granite 4.1 model card (huggingface.co/ibm-granite)
• LocalLLaMA Granite 4.1 release thread (reddit.com/r/LocalLLaMA, April 2026)
• HuggingFace open-llm-leaderboard scores (April 2026 snapshot)
• llama.cpp PR #12015 (Granite 4.1 chat template + tokenizer)
• TechPowerUp RTX 5090 / 4090 / 4060 Ti reviews