granite 4.2 3b

3.7B parameters · 128k context · Apache 2.0 · released 2026-08-25

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In short

granite 4.2 3b is an open model from IBM with 3.7B parameters. At Q4_K_M, granite 4.2 3b needs about 4 GB of memory at 8k context and 4 GB at 8k, counting the weights, the KV cache and runtime buffers. The cheapest hardware in Germany that runs granite 4.2 3b fully in memory at 8k context is the NVIDIA GeForce RTX 3060 12GB, 309 € at Amazon, at about 49 tok/s (estimated).

Updated . Speeds are estimates; how they are worked out.

What to run it on in Germany

Fully in memory at 8k context, priced at retailers today.

All hardware that runs it

Everything we price, best value first. Two cards split the model by layers.

HardwareQuantFitsDecodePricetok/s per 1,000
NVIDIA GeForce RTX 3060 12GBQ8_0fits49 tok/s309 €158.3
Intel Arc B580Q8_0fits50 tok/s360 €139.0
NVIDIA Tesla P40Q8_0fits47 tok/s369 €127.4
NVIDIA GeForce RTX 3080 10GBQ8_0fits103 tok/s500 €206.4
AMD Radeon RX 9060 XT 16GBQ8_0fits39 tok/s550 €70.2
Intel Arc A770 16GBQ8_0fits61 tok/s646 €95.1
NVIDIA GeForce RTX 4060 Ti 16GBQ8_0fits39 tok/s650 €60.2
NVIDIA GeForce RTX 5070Q8_0fits91 tok/s699 €130.6
NVIDIA GeForce RTX 4070 SuperQ8_0fits69 tok/s707 €96.9
NVIDIA GeForce RTX 5060 Ti 16GBQ8_0fits61 tok/s739 €82.4
AMD Radeon RX 9070 XTQ8_0fits77 tok/s778 €99.1
Mac mini M4 16GBQ8_0fits17 tok/s800 €21.4
AMD Radeon RX 7600 XTQ8_0fits35 tok/s813 €42.7
AMD Radeon RX 7900 XTQ8_0fits96 tok/s855 €112.8
AMD Radeon RX 7800 XTQ8_0fits75 tok/s1.002 €75.1
Intel Arc Pro B60Q8_0fits50 tok/s1.074 €46.5
NVIDIA GeForce RTX 4080 SuperQ8_0fits100 tok/s1.100 €90.9
NVIDIA GeForce RTX 5070 TiQ8_0fits122 tok/s1.149 €105.9
NVIDIA GeForce RTX 4070 Ti SuperQ8_0fits91 tok/s1.247 €73.2
Mac mini M4 32GBQ8_0fits17 tok/s1.400 €12.2
NVIDIA GeForce RTX 5080Q8_0fits130 tok/s1.500 €86.9
AMD Radeon RX 7900 XTXQ8_0fits116 tok/s1.555 €74.4
NVIDIA GeForce RTX 3090Q8_0fits127 tok/s1.850 €68.8
AMD Radeon AI PRO R9700Q8_0fits77 tok/s1.867 €41.3
NVIDIA GeForce RTX 3090 TiQ8_0fits137 tok/s2.500 €54.8
Mac mini M4 Pro 48GBQ8_0fits39 tok/s2.699 €14.4
Mac mini M4 Pro 64GBQ8_0fits39 tok/s2.799 €13.9
GMKtec EVO-X2 Ryzen AI Max+ 395 128GBQ8_0fits31 tok/s3.700 €8.4
NVIDIA GeForce RTX 4090Q8_0fits137 tok/s3.955 €34.6
Mac Studio M4 Max 64GBQ8_0fits78 tok/s4.183 €18.6
Mac Studio M4 Max 128GBQ8_0fits78 tok/s5.800 €13.4
Mac Studio M3 Ultra 96GBQ8_0fits117 tok/s5.999 €19.4
NVIDIA GeForce RTX 5090Q8_0fits243 tok/s6.028 €40.4
NVIDIA DGX Spark 128GBQ8_0fits37 tok/s8.313 €4.5
ASUS Ascent GX10 128GBQ8_0fits37 tok/s8.623 €4.3
Mac Studio M3 Ultra 256GBQ8_0fits117 tok/s15.555 €7.5
Mac Studio M3 Ultra 512GBQ8_0fits117 tok/s18.995 €6.1
NVIDIA RTX PRO 6000 BlackwellQ8_0fits243 tok/s19.076 €12.8
Framework Desktop Ryzen AI Max+ 395 128GBQ8_0fits31 tok/s––
Framework Desktop Ryzen AI Max+ 395 64GBQ8_0fits31 tok/s––

Memory needed

Weights, KV cache and runtime buffers at each context length (KV cache at fp16).

QuantFile@8k@32k@64k@128k
Q4_K_Mgguf2.2 GB3.5 GB5.5 GB8.2 GB13.6 GB
Q5_K_Mgguf2.5 GB3.9 GB5.9 GB8.6 GB13.9 GB
Q6_Kgguf2.9 GB4.3 GB6.3 GB9.0 GB14.4 GB
Q8_0gguf3.6 GB5.0 GB7.0 GB9.7 GB15.1 GB

Run it

Copy a command for the runtime you use.

  • Q4_K_M
    • Ollamaollama run hf.co/bartowski/granite-4.2-3b-GGUF:Q4_K_M
    • LM Studiolms get bartowski/granite-4.2-3b-GGUF@q4_k_m
    • llama.cppllama-server -hf bartowski/granite-4.2-3b-GGUF:Q4_K_M
  • Q5_K_M
    • Ollamaollama run hf.co/bartowski/granite-4.2-3b-GGUF:Q5_K_M
    • LM Studiolms get bartowski/granite-4.2-3b-GGUF@q5_k_m
    • llama.cppllama-server -hf bartowski/granite-4.2-3b-GGUF:Q5_K_M
  • Q6_K
    • Ollamaollama run hf.co/bartowski/granite-4.2-3b-GGUF:Q6_K
    • LM Studiolms get bartowski/granite-4.2-3b-GGUF@q6_k
    • llama.cppllama-server -hf bartowski/granite-4.2-3b-GGUF:Q6_K
  • Q8_0
    • Ollamaollama run hf.co/bartowski/granite-4.2-3b-GGUF:Q8_0
    • LM Studiolms get bartowski/granite-4.2-3b-GGUF@q8_0
    • llama.cppllama-server -hf bartowski/granite-4.2-3b-GGUF:Q8_0

Notes

Native context is 128K, and the card also claims a long-context extension to 512K; its serving examples use 131072. The vLLM reasoning parser needs vLLM v0.20+ (or SGLang v0.5.18+), and the recommended sampling is temperature=1.0 and top_p=0.95.

Questions

How much VRAM does granite 4.2 3b need?
At Q4_K_M, granite 4.2 3b needs about 4 GB of memory at 8k context and 4 GB at 8k, counting the weights, the KV cache and runtime buffers.
What is the best GPU for running granite 4.2 3b locally?
The cheapest hardware in Germany that runs granite 4.2 3b fully in memory at 8k context is the NVIDIA GeForce RTX 3060 12GB, 309 € at Amazon, at about 49 tok/s (estimated). The best value is the NVIDIA GeForce RTX 3080 10GB (about 103 tok/s for 500 €). The fastest we price is the NVIDIA GeForce RTX 5090, at about 243 tok/s.
Can I run granite 4.2 3b on a 16 GB GPU?
Yes: at Q8_0 it fits fully in 16 GB at 8k context. On the NVIDIA GeForce RTX 5080 it runs at about 130 tok/s (estimated).
Can I run granite 4.2 3b on a 24 GB GPU?
Yes: at Q8_0 it fits fully in 24 GB at 8k context. On the NVIDIA GeForce RTX 3090 Ti it runs at about 137 tok/s (estimated).
How do I run granite 4.2 3b locally?
With Ollama: ollama run hf.co/bartowski/granite-4.2-3b-GGUF:Q4_K_M (the Q4_K_M build). LM Studio and llama.cpp commands are on this page.
What licence is granite 4.2 3b released under?
Apache 2.0, which allows commercial use.