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

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 on April 29, 2026, slots in three new sizes — 3B, 8B, 30B — all under a plain Apache 2.0 license. The kicker is that IBM separately offers standard contractual IP indemnification for IBM-developed models, similar to its hardware and software products. Llama 3.1, by contrast, ships under the Llama Community License with a >700M-MAU carve-out; Granite's combination of unrestricted Apache 2.0 plus an enterprise-grade indemnity offering is unusual in the local-LLM space. 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 ~15T-token mix (five phases of pre-training and annealing) 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 roughly 20+ tok/s — comfortably usable for chat.
• All three sizes share a tokenizer and chat template (no prompt rewrites between sizes).
• Apache 2.0 release plus IBM's contractual IP indemnification 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 3.0 shipped at 4K native; Granite 3.1 stretched to 128K via continued pre-training. Granite 4.1 bakes 131,072-token context into the base training run.

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.

On the Pi 5, the Hailo-8 path runs through Hailo's own runtime (HailoRT / the AI HAT+ stack), not llama.cpp directly — llama.cpp itself doesn't yet support Hailo offload. Pure-CPU llama.cpp on the Pi 5 sits around 4-5 tok/s on the 8B; switching to Hailo's runtime gets you to roughly 11 tok/s, which 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, dedicated code models like Qwen2.5-Coder-32B remain stronger at code-specific benchmarks than Granite 4.1 30B.

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

Products mentioned

• Freenove Ultimate Starter Kit for Raspberry Pi 5 4 B 3 B+…
• Freenove Ultimate Starter Kit for Raspberry Pi 5 4 B 3 B+…
• Freenove Ultimate Starter Kit for Raspberry Pi 5 4 B 3 B+…