Granite 4.0 H-Tiny 7B-A1B vs DeepSeek R1 Distill 7B
Side-by-side specs, benchmarks, and a verdict by use case.
Updated 2026-07-13
| Spec | Granite 4.0 H-Tiny 7B-A1B | DeepSeek R1 Distill 7B |
|---|---|---|
| Parameters | 7B | 7B |
| Author | IBM | DeepSeek |
| License | Apache 2.0 | MIT |
| Context window | 0k | 0k |
| VRAM at Q4 | 4 GB | 5 GB |
| VRAM at Q5 | 5 GB | 6 GB |
| VRAM at Q8 | 7 GB | 9 GB |
| VRAM at FP16 | 14 GB | 16 GB |
| Use cases | chat, general, moe, small | reasoning |
Verdict
Both models sit in a similar size class. The pick depends on tags, license, and benchmarks rather than raw parameter count.
The two models at a glance
About Granite 4.0 H-Tiny 7B-A1B
IBM's edge-class hybrid MoE with 7B total and only 1B active parameters — Apache 2.0 licensed and built for embedded and low-cost serving. Strengths: Extremely low compute cost per token via 1B active params, Apache 2.0 license with no commercial strings attached, 128k context handled efficiently thanks to hybrid Mamba-2, Tiny memory footprint suits edge and serverless deploys.
About DeepSeek R1 Distill 7B
A 7B DeepSeek model distilled from R1 671B with explicit chain-of-thought reasoning. Surprisingly strong on AIME and MATH for its size. Strengths: Explicit chain-of-thought reasoning at 7B scale, Strong AIME and MATH scores for its size, 32k context, MIT license.
How they compare
Granite 4.0 H-Tiny 7B-A1B comes from IBM and DeepSeek R1 Distill 7B from DeepSeek, they belong to the Granite and DeepSeek families respectively. This comparison is built entirely from structured specs — parameter count, VRAM by quantization, context window, license, and published benchmark scores — so the verdict below reflects measurable differences rather than marketing claims.
Granite 4.0 H-Tiny 7B-A1B and DeepSeek R1 Distill 7B share the same 7B parameter class. At Q4, Granite 4.0 H-Tiny 7B-A1B fits in about 4 GB of VRAM versus 5 GB for the other — a 1 GB difference that matters on consumer GPUs.
The two models target different sweet spots: Granite 4.0 H-Tiny 7B-A1B is tuned for chat, general, moe, small, while DeepSeek R1 Distill 7B leans toward reasoning. Match the model to your dominant workload rather than to raw size.
On a typical mid-range GPU, Granite 4.0 H-Tiny 7B-A1B pushes roughly 180 tokens/sec versus 35, so it is the more responsive choice for interactive or high-volume use. For long-context work, Granite 4.0 H-Tiny 7B-A1B offers the bigger window (125k vs 32k tokens).
Memory, quantization & throughput
Across quantization levels, Granite 4.0 H-Tiny 7B-A1B requires Q4 ≈ 4 GB, Q5 ≈ 5 GB, Q8 ≈ 7 GB, FP16 ≈ 14 GB, while DeepSeek R1 Distill 7B requires Q4 ≈ 5 GB, Q5 ≈ 6 GB, Q8 ≈ 9 GB, FP16 ≈ 16 GB. In practice Granite 4.0 H-Tiny 7B-A1B fits an 8 GB card at Q4, so plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity of Q8 or FP16.
Without a GPU, Granite 4.0 H-Tiny 7B-A1B needs roughly 8 GB of system RAM to run on CPU and DeepSeek R1 Distill 7B about 8 GB — workable for offline use but far slower than GPU inference. On a mid-range GPU you can expect on the order of 180 tokens/sec from Granite 4.0 H-Tiny 7B-A1B and 35 from DeepSeek R1 Distill 7B, scaling up to 350 and 90 tokens/sec on high-end hardware.
Which fits your GPU
Here is the highest-quality quantization of each model that fits common GPU memory budgets, so you can match Granite 4.0 H-Tiny 7B-A1B or DeepSeek R1 Distill 7B to the card you actually own:
- On a 8 GB GPU: Granite 4.0 H-Tiny 7B-A1B runs at Q8 (7 GB); DeepSeek R1 Distill 7B runs at Q5 (6 GB).
- On a 12 GB GPU: Granite 4.0 H-Tiny 7B-A1B runs at Q8 (7 GB); DeepSeek R1 Distill 7B runs at Q8 (9 GB).
- On a 16 GB GPU: Granite 4.0 H-Tiny 7B-A1B runs at FP16 (14 GB); DeepSeek R1 Distill 7B runs at FP16 (16 GB).
- On a 24 GB GPU: Granite 4.0 H-Tiny 7B-A1B runs at FP16 (14 GB); DeepSeek R1 Distill 7B runs at FP16 (16 GB).
Benchmark scores
Reported benchmarks for DeepSeek R1 Distill 7B: AIME 2024 55.5, MATH-500 92.8.
Bottom line: which should you pick?
- Pick Granite 4.0 H-Tiny 7B-A1B for long-context work (up to 125k tokens).
- Pick Granite 4.0 H-Tiny 7B-A1B if your workload is chat, general, moe, small.
- Pick DeepSeek R1 Distill 7B if your workload is reasoning.
Which GPU should you buy to run DeepSeek R1 Distill 7B?
To run DeepSeek R1 Distill 7B locally at Q4, you need ~5 GB of VRAM. The best value for this is a RTX 5060 (8 GB VRAM).
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Frequently asked questions
What is the difference between Granite 4.0 H-Tiny 7B-A1B and DeepSeek R1 Distill 7B?
The headline differences: both are 7B models; their context windows differ (125k vs 32k tokens); they ship under different licenses (Apache 2.0 vs MIT). Below we break down VRAM by quantization, benchmark scores, and a use-case verdict so you can pick the right one.
Can Granite 4.0 H-Tiny 7B-A1B and DeepSeek R1 Distill 7B run on a 24 GB GPU?
At a Q4 quantization, Granite 4.0 H-Tiny 7B-A1B needs about 4 GB of VRAM and fits comfortably on a 24 GB GPU; DeepSeek R1 Distill 7B needs about 5 GB and fits comfortably on a 24 GB GPU. Granite 4.0 H-Tiny 7B-A1B is the lighter option for tight VRAM budgets.
What licenses do Granite 4.0 H-Tiny 7B-A1B and DeepSeek R1 Distill 7B use?
Granite 4.0 H-Tiny 7B-A1B is licensed under Apache 2.0 and DeepSeek R1 Distill 7B under MIT.
Which has the longer context window, Granite 4.0 H-Tiny 7B-A1B or DeepSeek R1 Distill 7B?
Granite 4.0 H-Tiny 7B-A1B has the larger context window (125k vs 32k tokens), so it handles longer documents and codebases in a single prompt.