Granite 4.0 H-Tiny 7B-A1BvsDeepSeek R1 Distill 7B
Complete comparison between Granite 4.0 H-Tiny 7B-A1B (7B parameters, IBM) and DeepSeek R1 Distill 7B (7B, DeepSeek). VRAM required by quantization, tokens per second measured on 4 reference GPUs, verdict by use case, license, installation commands. All figures are calculated from the catalog data—nothing is copied and pasted between pages.
In brief
| Feature | Granite 4.0 H-Tiny 7B-A1B | DeepSeek R1 Distill 7B |
|---|---|---|
| Settings | 7B | 7B |
| Family | Granite | DeepSeek |
| Author | IBM | DeepSeek |
| Origin | US | CN |
| License | Apache 2.0 | MIT |
| Context | 128,000 tokens | 32 768 tokens |
| Output | October 2025 | January 2025 |
Memory footprint
Approximate VRAM required for inference with an average context window. The winner (in green) is the model that uses less — smaller models have the edge.
| Quantization | Granite 4.0 H-Tiny 7B-A1B | DeepSeek R1 Distill 7B |
|---|---|---|
| Q4_K_M (lightweight) | 4 GB | 5 GB |
| Q5_K_M (balanced) | 5 GB | 6 GB |
| Q8 (near-lossless) | 7 GB | 9 GB |
| FP16 (max quality) | 14 GB | 16 GB |
| CPU-only RAM | 8 GB | 8 GB |
Estimated speed (tokens/second)
Estimates based on the best quantization that can fit on each GPU. Actual figures depend on the prompt, context, and engine (llama.cpp, vLLM, MLX). Methodology.
| Reference GPU | Granite 4.0 H-Tiny 7B-A1B | DeepSeek R1 Distill 7B |
|---|---|---|
| RTX 4090 (24 GB) | 350 tok/s · FP16 | 90 tok/s · FP16 |
| RTX 4080 (16 GB) | 180 tok/s · FP16 | 35 tok/s · FP16 |
| RTX 3060 12GB (12 GB) | 60 tok/s · Q8 | 12 tok/s · Q8 |
| Apple M4 Pro (48 GB) (36 GB) | 180 tok/s · FP16 | 35 tok/s · FP16 |
Verdict by use case
For each common use case, we indicate which of the two is better suited based on its tags, size, and specialization.
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Strengths and weaknesses
Granite 4.0 H-Tiny 7B-A1B
Hybrid 7B/1B active. Extremely low compute cost. Edge/embedded.
- Very low compute cost
- Apache 2.0
- Efficient long context
- Below 3B dense quality for certain tasks
Installation
DeepSeek R1 Distill 7B
Distilled chain-of-thought reasoning. Impressive at math.
- Explicit CoT reasoning in 7B
- MIT License
- 32k ctx
- Surprisingly strong on AIME/MATH for its size
- Very verbose (thinking tokens)
- Worse than R1 32B at complex reasoning
Installation
Frequently asked questions
Which model runs best on RTX 4090 (24 GB): Granite 4.0 H-Tiny 7B-A1B or DeepSeek R1 Distill 7B?
On an RTX 4090, Granite 4.0 H-Tiny 7B-A1B runs at FP16 (~350 tok/s), DeepSeek R1 Distill 7B in FP16 (~90 tok/s). In pure throughput, Granite 4.0 H-Tiny 7B-A1B wins. See the configurator to test your exact GPU.
Which uses the least VRAM?
In Q4_K_M, Granite 4.0 H-Tiny 7B-A1B fits in 4 GB versus 5 GB for its rival — a 1 GB gap, significant if you're targeting an RTX 3060 or a 4060 Ti 8 GB.
Can these models be used in commercial production?
Granite 4.0 H-Tiny 7B-A1B is under Apache 2.0 — free for commercial use. DeepSeek R1 Distill 7B is also free under the MIT license. For SaaS, favor Apache 2.0 or MIT.
Which one should you choose in 2026?
Depends on your primary constraint. Smaller / faster: Granite 4.0 H-Tiny 7B-A1B (7B). More capable: Granite 4.0 H-Tiny 7B-A1B (7B). If you're unsure, run it configurator with your GPU and use case — it will decide based on both.
Alternatives to consider
If neither works for you, here are some neighboring models you could explore.