Lab · LLM VRAM · NVIDIA
ComputedWhat LLMs can the Jetson AGX Orin (64GB) run?
59 of 62 rostered models have a usable on-device fit. The table keeps all 62 visible, including 0 below-context fits and 3 that exceed this device, so “not listed” never masquerades as an answer.
Published memory
64 GB
LPDDR5 unified
Assumed usable
48.0 GiB
75% of capacity
Peak bandwidth
204.8 GB/s
theoretical device spec
Usable fits
59 / 62
largest: 72.7B at Q4_K_M
Every model, solved on this accelerator
Usable rows fit weights plus at least 4k tokens, capped at the model’s own trained window. Tight rows hold weights but fall below that floor. The speed column is a memory-bandwidth ceiling, not a benchmark.
59 usable · 0 tight · 3 no fit
| Model | Parameters | On-device result | Selected weights | Max context | Decode ceiling · read per token |
|---|---|---|---|---|---|
| Qwen2.5 72B InstructQwen | 72.7B | Quantized fit | Q4_K_M · 44.2 GiBpublished file | 12k | 4 tok/sreads 46.7 GiB/token |
| DeepSeek-R1-Distill-Llama 70BDeepSeek | 70.6B | Quantized fit | Q5_K_M · 46.5 GiBpublished file | 5k | 4 tok/sreads 48.0 GiB/token |
| Llama 3.1 70B InstructLlama | 70.6B | Quantized fit | Q5_K_M · 46.5 GiBpublished file | 5k | 4 tok/sreads 48.0 GiB/token |
| Llama 3.1 Nemotron 70B InstructNemotron | 70.6B | Quantized fit | Q5_K_M · 46.5 GiBpublished file | 5k | 4 tok/sreads 48.0 GiB/token |
| Llama 3.3 70B InstructLlama | 70.6B | Quantized fit | Q5_K_M · 46.5 GiBpublished file | 5k | 4 tok/sreads 48.0 GiB/token |
| Mixtral 8x7B InstructMistral | 46.7B | Quantized fit | Q8_0 · 46.2 GiBarchitecture calculation | 14k | 14 tok/sreads 13.7 GiB/token |
| Hermes 4.3 36BSeed OSS | 36.2B | Quantized fit | Q8_0 · 35.8 GiBarchitecture calculation | 49k | 5 tok/sreads 37.8 GiB/token |
| KAT Coder V2.5 DevQwen3 5 MOE Text | 34.7B | Quantized fit | Q8_0 · 34.3 GiBarchitecture calculation | 175k | 55 tok/sreads 3.4 GiB/token |
| Qwen AgentWorld 35B A3BQwen3 5 MOE Text | 34.7B | Quantized fit | Q8_0 · 34.4 GiBpublished file | 174k | 55 tok/sreads 3.5 GiB/token |
| Yi 1.5 34B ChatYi | 34.4B | Quantized fit | Q8_0 · 34.0 GiBpublished file | 4k | 5 tok/sreads 35.0 GiB/token |
| Laguna XS 2.1Laguna | 33.4B | Quantized fit | Q8_0 · 33.2 GiBpublished file | 256k | 65 tok/sreads 2.9 GiB/token |
| DeepSeek-R1-Distill-Qwen 32BDeepSeek | 32.8B | Quantized fit | Q8_0 · 32.4 GiBpublished file | 62k | 6 tok/sreads 34.4 GiB/token |
| Qwen2.5 32B InstructQwen | 32.8B | Quantized fit | Q8_0 · 32.4 GiBpublished file | 32k | 6 tok/sreads 34.4 GiB/token |
| Qwen2.5-Coder 32B InstructQwen | 32.8B | Quantized fit | Q8_0 · 32.4 GiBpublished file | 32k | 6 tok/sreads 34.4 GiB/token |
| QwQ 32BQwen | 32.8B | Quantized fit | Q8_0 · 32.4 GiBarchitecture calculation | 40k | 6 tok/sreads 34.4 GiB/token |
| Qwen3 32BQwen | 32.8B | Quantized fit | Q8_0 · 32.4 GiBarchitecture calculation | 40k | 6 tok/sreads 34.4 GiB/token |
| Qwen3 30B-A3BQwen | 30.5B | Quantized fit | Q8_0 · 30.3 GiBpublished file | 40k | 47 tok/sreads 4.1 GiB/token |
| Gemma 3 27B InstructGemma | 27.4B | Quantized fit | Q8_0 · 27.1 GiBarchitecture calculation | 128k | 7 tok/sreads 28.2 GiB/token |
| Gemma 2 27B InstructGemma | 27.2B | Quantized fit | Q8_0 · 27.0 GiBpublished file | 8k | 6 tok/sreads 29.8 GiB/token |
| Dolphin Mistral 24B Venice EditionMistral | 24B | Full precision · limited context | FP16 / BF16 · 44.7 GiBpublished file | 21k | 4 tok/sreads 46.0 GiB/token |
| Mistral Small 24B InstructMistral | 23.6B | Full precision · limited context | FP16 / BF16 · 43.9 GiBpublished file | 26k | 4 tok/sreads 45.2 GiB/token |
| gpt-oss 20Bgpt-oss | 21.5B | Full precision · comfortable | As released · 12.8 GiBpublished file | 128k | 69 tok/sreads 2.8 GiB/token |
| DeepSeek-R1-Distill-Qwen 14BDeepSeek | 14.8B | Full precision · comfortable | FP16 / BF16 · 27.5 GiBpublished file | 109k | 7 tok/sreads 29.0 GiB/token |
| Qwen2.5 14B InstructQwen | 14.8B | Full precision · comfortable | FP16 / BF16 · 27.5 GiBpublished file | 32k | 7 tok/sreads 29.0 GiB/token |
| Qwen3 14BQwen | 14.8B | Full precision · comfortable | FP16 / BF16 · 27.5 GiBpublished file | 40k | 7 tok/sreads 28.8 GiB/token |
| Phi-4 14BPhi | 14.7B | Full precision · comfortable | FP16 / BF16 · 27.3 GiBpublished file | 16k | 7 tok/sreads 28.9 GiB/token |
| OLMo 2 13B InstructOLMo | 13.7B | Full precision · comfortable | FP16 / BF16 · 25.5 GiBpublished file | 4k | 7 tok/sreads 28.7 GiB/token |
| Mistral Nemo 12B InstructMistral | 12.2B | Full precision · comfortable | FP16 / BF16 · 22.8 GiBpublished file | 128k | 8 tok/sreads 24.1 GiB/token |
| Gemma 3 12B InstructGemma | 12.2B | Full precision · comfortable | FP16 / BF16 · 22.7 GiBpublished file | 128k | 8 tok/sreads 23.5 GiB/token |
| Qwythos 9B Claude Mythos 5 1MQwen3 5 Text | 9.4B | Full precision · comfortable | FP16 / BF16 · 17.5 GiBpublished file | 244k | 10 tok/sreads 18.5 GiB/token |
| Gemma 2 9B InstructGemma | 9.2B | Full precision · comfortable | FP16 / BF16 · 17.2 GiBpublished file | 8k | 10 tok/sreads 19.8 GiB/token |
| Qwen3 8BQwen | 8.2B | Full precision · comfortable | FP16 / BF16 · 15.3 GiBpublished file | 40k | 12 tok/sreads 16.4 GiB/token |
| Granite 3.3 8B InstructGranite | 8.2B | Full precision · comfortable | FP16 / BF16 · 15.2 GiBpublished file | 128k | 12 tok/sreads 16.5 GiB/token |
| DeepSeek-R1-Distill-Llama 8BDeepSeek | 8B | Full precision · comfortable | FP16 / BF16 · 15.0 GiBpublished file | 128k | 12 tok/sreads 16.0 GiB/token |
| Llama 3.1 8B InstructLlama | 8B | Full precision · comfortable | FP16 / BF16 · 15.0 GiBpublished file | 128k | 12 tok/sreads 16.0 GiB/token |
| DeepSeek-R1-Distill-Qwen 7BDeepSeek | 7.6B | Full precision · comfortable | FP16 / BF16 · 14.2 GiBpublished file | 128k | 13 tok/sreads 14.6 GiB/token |
| Qwen2.5 7B InstructQwen | 7.6B | Full precision · comfortable | FP16 / BF16 · 14.2 GiBpublished file | 32k | 13 tok/sreads 14.6 GiB/token |
| Qwen2.5-Coder 7B InstructQwen | 7.6B | Full precision · comfortable | FP16 / BF16 · 14.2 GiBpublished file | 32k | 13 tok/sreads 14.6 GiB/token |
| Falcon3 7B InstructFalcon | 7.5B | Full precision · comfortable | FP16 / BF16 · 13.9 GiBpublished file | 32k | 13 tok/sreads 14.8 GiB/token |
| Mistral 7B Instruct v0.3Mistral | 7.2B | Full precision · comfortable | FP16 / BF16 · 13.5 GiBpublished file | 32k | 13 tok/sreads 14.5 GiB/token |
| Gemma 3 4B InstructGemma | 4.3B | Full precision · comfortable | FP16 / BF16 · 8.0 GiBpublished file | 128k | 23 tok/sreads 8.3 GiB/token |
| Nanbeige4.2 3BNanbeige | 4.2B | Full precision · comfortable | FP16 / BF16 · 7.8 GiBpublished file | 256k | 23 tok/sreads 8.5 GiB/token |
| Qwen3 4BQwen | 4B | Full precision · comfortable | FP16 / BF16 · 7.5 GiBpublished file | 40k | 22 tok/sreads 8.6 GiB/token |
| Phi-4-mini 3.8B InstructPhi | 3.8B | Full precision · comfortable | FP16 / BF16 · 7.1 GiBpublished file | 128k | 23 tok/sreads 8.1 GiB/token |
| Phi-3.5-mini 3.8B InstructPhi | 3.8B | Full precision · comfortable | FP16 / BF16 · 7.1 GiBpublished file | 109k | 19 tok/sreads 10.1 GiB/token |
| Llama 3.2 3B InstructLlama | 3.2B | Full precision · comfortable | FP16 / BF16 · 6.0 GiBpublished file | 128k | 28 tok/sreads 6.9 GiB/token |
| Qwen2.5 3B InstructQwen | 3.1B | Full precision · comfortable | FP16 / BF16 · 5.7 GiBpublished file | 32k | 32 tok/sreads 6.0 GiB/token |
| Qwen3 1.7BQwen | 2B | Full precision · comfortable | FP16 / BF16 · 3.8 GiBpublished file | 40k | 41 tok/sreads 4.7 GiB/token |
| DeepSeek-R1-Distill-Qwen 1.5BDeepSeek | 1.8B | Full precision · comfortable | FP16 / BF16 · 3.3 GiBpublished file | 128k | 54 tok/sreads 3.5 GiB/token |
| SmolLM2 1.7B InstructSmolLM | 1.7B | Full precision · comfortable | FP16 / BF16 · 3.2 GiBpublished file | 8k | 41 tok/sreads 4.7 GiB/token |
| Qwen2.5 1.5B InstructQwen | 1.5B | Full precision · comfortable | FP16 / BF16 · 2.9 GiBpublished file | 32k | 62 tok/sreads 3.1 GiB/token |
| Llama 3.2 1B InstructLlama | 1.2B | Full precision · comfortable | FP16 / BF16 · 2.3 GiBpublished file | 128k | 75 tok/sreads 2.6 GiB/token |
| TinyLlama 1.1B ChatTinyLlama | 1.1B | Full precision · comfortable | FP16 / BF16 · 2.0 GiBpublished file | 2k | 91 tok/sreads 2.1 GiB/token |
| MiniCPM5 1BLlama | 1.1B | Full precision · comfortable | FP16 / BF16 · 2.0 GiBpublished file | 128k | 87 tok/sreads 2.2 GiB/token |
| Gemma 3 1B InstructGemma | 1000M | Full precision · comfortable | FP16 / BF16 · 1.9 GiBpublished file | 32k | 100 tok/sreads 1.9 GiB/token |
| Qwen3 0.6BQwen | 752M | Full precision · comfortable | FP16 / BF16 · 1.4 GiBpublished file | 40k | 84 tok/sreads 2.3 GiB/token |
| Qwen2.5 0.5B InstructQwen | 494M | Full precision · comfortable | FP16 / BF16 · 942 MiBpublished file | 32k | 188 tok/sreads 1.0 GiB/token |
| SmolLM2 360M InstructSmolLM | 362M | Full precision · comfortable | FP16 / BF16 · 690 MiBpublished file | 8k | 193 tok/sreads 1010 MiB/token |
| SmolLM2 135M InstructSmolLM | 135M | Full precision · comfortable | FP16 / BF16 · 257 MiBpublished file | 8k | 447 tok/sreads 437 MiB/token |
| Hy3HY V3 | 298.8B | No on-device fit | Q3_K_M · 128.1 GiBpublished file | — | Not resident— |
| Laguna S 2.1Laguna | 117.6B | No on-device fit | Q3_K_M · 50.3 GiBpublished file | — | Not resident— |
| gpt-oss 120Bgpt-oss | 116.8B | No on-device fit | Q3_K_M · 58.3 GiBpublished file | — | Not resident— |
Device specification and assumption
- Memory
- 64 GB LPDDR5 unified
- Bandwidth
- 204.8 GB/s
- Usable budget
- 75% → 48.0 GiB
Capacity and bandwidth come from the vendor specification. The usable fraction is an explicit planning assumption, not a device specification.
What the table does — and does not — claim
Weight sizes use published checkpoint or GGUF files where the roster has one, otherwise the documented bits-per-weight calculation. Context comes from each model’s layer and KV-head geometry. The decode figure is the card’s peak bandwidth divided by the bytes one token reads — the weights it routes through plus one pass over the KV cache, printed under each ceiling — so it is a roofline bound, not measured application throughput. Multi-GPU splitting and host-memory offload are outside this on-device table.
Questions about this card
What LLMs can the Jetson AGX Orin (64GB) run?
59 of the 62 open models in this roster have an on-device configuration with usable context. The largest by exact parameter count is Qwen2.5 72B Instruct at Q4_K_M, with room for 12k tokens. “Largest” describes parameter count, not model quality or task performance.
How much memory is usable on the Jetson AGX Orin (64GB)?
The device publishes 64 GB of LPDDR5 unified. This calculator budgets 75%, or 48.0 GiB, for model weights and KV cache; the remainder is an explicit allowance for the runtime, driver or operating system, workspace, and display. It is an assumption rather than a vendor specification.
Are the speed figures benchmarks for the Jetson AGX Orin (64GB)?
No. They are bandwidth-bound roofline ceilings: 204.8 GB/s divided by the bytes one decoded token actually reads, which the table prints beside every ceiling. That is not the size of the file on disk — a token reads the weights it routes through, all of them for a dense model but only the selected experts for a mixture-of-experts, plus one pass over the KV cache. Laguna XS 2.1 holds 33.2 GiB resident at Q8_0 yet reads 2.9 GiB/token, which is what produces its 65 tok/s. Real throughput is lower because kernels, cache traffic, prompt processing, scheduling, and runtime overhead also consume time.