tokyotech-llm
Llama-3.1-Swallow-8B-Instruct-v0.5
- Parameters
- 8B
- Max context length
- 131,072
- Architecture
- llama
- License
- Conditional
- License
- llama3.3
About Llama-3.1-Swallow-8B-Instruct-v0.5
Llama-3.1-Swallow-8B-Instruct-v0.5 is a GGUF model based on tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.5 on Hugging Face. GGUF files are published by 4 sources (mmnga/Llama-3.1-Swallow-8B-Instruct-v0.5-gguf, mradermacher/Llama-3.1-Swallow-8B-Instruct-v0.5-GGUF, tensorblock/tokyotech-llm_Llama-3.1-Swallow-8B-Instruct-v0.5-GGUF and others). It has about 8B parameters, the llama architecture, a maximum context length of 131,072 tokens (values written in the GGUF).
There are 27 quantizations, with file sizes from 2.02 GB (IQ1_S) to 16.1 GB (F16) (actual sizes on Hugging Face).
Running Q4_K_M (4.92 GB) with an 8,192-token context needs an estimated 6.79 GB of memory, and an estimated 10.0 GB at 32,768 tokens. For 8,192 tokens, a GPU with 8 GB or more of VRAM (such as GeForce RTX 3070 Ti, Arc A580), or a Mac with 8 GB or more of unified memory, is a rough guide (macOS caps how much memory the GPU can use, so leave some headroom). The smallest, IQ1_S (2.02 GB), needs an estimated 3.89 GB at 8,192 tokens.
The original model is licensed under Llama 3.3 Community License, which allows commercial use with conditions such as scale or purpose. Check the original license for the terms of use.
File size and estimated memory per quantization
- Up to 8 GB
- Up to 16 GB
- Up to 24 GB
- Up to 48 GB
- Up to 128 GB
- More
File sizes are the actual values on Hugging Face. Memory is estimated for each context length (KV cache f16). The formula is in How memory is calculated.
All memory figures are Estimated (KV cache f16). It should run on a setup with at least this much VRAM (or Mac unified memory). Click a value to see the devices and models that fit that amount (Find by GPU / Mac).
4 sources publish GGUF files of this model. Quantization types and file sizes differ slightly between sources, so there is a table for each source below.
About the "quality guide" (how far a quantization drifts from the original)
For each quantization type, how far the output drifts from the original model, grouped into six levels from values llama.cpp measured on Llama-3-8B.
The value is mean KLD (the difference between the original and quantized model in next-token probabilities); smaller means closer to the original. Quantizations made with an imatrix (importance data) come out slightly smaller. Levels use the value without an imatrix (or with one, for types that only have that).
These values were not measured on this model. Larger models and MoE models tend to drift less with the same type. Distributor-specific mixed types (such as UD-Q4_K_XL) cannot be matched by type name alone, so they show "-".
| Quantization | Quality guide | KLD (no imatrix) | KLD (imatrix) | Size (Llama-3-8B) |
|---|---|---|---|---|
| Q8_0 | Nearly identical | 0.0014 | - | 7.96 GiB |
| Q6_K | Very small | 0.0055 | - | 6.14 GiB |
| Q5_K_M | Very small | 0.0108 | - | 5.33 GiB |
| Q5_K_S | Small | 0.0166 | - | 5.21 GiB |
| Q5_1 | Small | 0.0180 | - | 5.65 GiB |
| Q5_0 | Small | 0.0222 | - | 5.21 GiB |
| Q4_K_M | Small | 0.0313 | 0.0282 | 4.58 GiB |
| Q4_K_S | Medium | 0.0431 | 0.0320 | 4.37 GiB |
| IQ4_NL | Small | - | 0.0357 | 4.35 GiB |
| IQ4_XS | Small | - | 0.0363 | 4.14 GiB |
| Q4_1 | Medium | 0.0717 | - | 4.78 GiB |
| Q4_0 | Medium | 0.0719 | - | 4.34 GiB |
| Q3_K_L | Medium | 0.0872 | 0.0731 | 4.03 GiB |
| Q3_K_M | Medium | 0.1019 | 0.0844 | 3.74 GiB |
| IQ3_M | Medium | - | 0.1025 | 3.53 GiB |
| IQ3_S | Medium | - | 0.1113 | 3.42 GiB |
| IQ3_XS | Large | - | 0.1387 | 3.28 GiB |
| IQ3_XXS | Large | - | 0.1836 | 3.05 GiB |
| Q3_K_S | Large | 0.2282 | 0.1998 | 3.41 GiB |
| IQ2_M | Large | - | 0.3260 | 2.74 GiB |
| Q2_K | Very large | 0.4451 | 0.3322 | 2.96 GiB |
| Q2_K_S | Very large | - | 0.4034 | 2.96 GiB |
| IQ2_S | Very large | - | 0.4392 | 2.56 GiB |
| IQ2_XS | Very large | - | 0.5463 | 2.43 GiB |
| IQ2_XXS | Very large | - | 0.8120 | 2.24 GiB |
| IQ1_M | Very large | - | 1.3931 | 2.01 GiB |
| IQ1_S | Very large | - | 2.2546 | 1.88 GiB |
Source: llama.cpp (MIT), measured on Llama-3-8B against FP16, Wikitext-2, revision f364eb6f. Original table · Reading and choosing quantizations
GGUF source
mmnga/Llama-3.1-Swallow-8B-Instruct-v0.5-gguf
Converted and published by a third party About this label
Last checked: Nov 11, 2026
- Architecture
- llama
- Supported before llama.cpp b4409
- Parameters
- 8.0B
- Max context length
- 131,072
- License
- llama3.3 Conditional
Ollama ollama run hf.co/mmnga/Llama-3.1-Swallow-8B-Instruct-v0.5-gguf:Q4_K_M
llama.cpp llama-server -hf mmnga/Llama-3.1-Swallow-8B-Instruct-v0.5-gguf:Q4_K_M
GGUF source
mradermacher/Llama-3.1-Swallow-8B-Instruct-v0.5-GGUF
Converted and published by a third party About this label
Last checked: Nov 11, 2026
- Architecture
- llama
- Supported before llama.cpp b4409
- Parameters
- 8.0B
- Max context length
- 131,072
- License
- llama3.3 Conditional
| Quantization | File size Measured | Quality guide | 4,096 | 8,192 | 16,384 | 32,768 | 65,536 | 131,072 max |
|---|---|---|---|---|---|---|---|---|
| Q2_K | 3.18 GB | Very large | 4.52 GB | 5.05 GB | 6.13 GB | 8.27 GB | 12.6 GB | 21.2 GB |
| Q3_K_S | 3.66 GB | Large | 5.00 GB | 5.54 GB | 6.61 GB | 8.76 GB | 13.1 GB | 21.6 GB |
| Q3_K_M | 4.02 GB | Medium | 5.36 GB | 5.89 GB | 6.97 GB | 9.11 GB | 13.4 GB | 22.0 GB |
| Q3_K_L | 4.32 GB | Medium | 5.66 GB | 6.20 GB | 7.27 GB | 9.42 GB | 13.7 GB | 22.3 GB |
| IQ4_XS | 4.48 GB | Small | 5.82 GB | 6.36 GB | 7.43 GB | 9.58 GB | 13.9 GB | 22.5 GB |
| Q4_K_S | 4.69 GB | Medium | 6.03 GB | 6.57 GB | 7.64 GB | 9.79 GB | 14.1 GB | 22.7 GB |
| Q4_K_M | 4.92 GB | Small | 6.26 GB | 6.79 GB | 7.87 GB | 10.0 GB | 14.3 GB | 22.9 GB |
| Q5_K_S | 5.60 GB | Small | 6.94 GB | 7.47 GB | 8.55 GB | 10.7 GB | 15.0 GB | 23.6 GB |
| Q5_K_M | 5.73 GB | Very small | 7.07 GB | 7.61 GB | 8.68 GB | 10.8 GB | 15.1 GB | 23.7 GB |
| Q6_K | 6.60 GB | Very small | 7.93 GB | 8.47 GB | 9.54 GB | 11.7 GB | 16.0 GB | 24.6 GB |
| Q8_0 | 8.54 GB | Nearly identical | 9.88 GB | 10.4 GB | 11.5 GB | 13.6 GB | 17.9 GB | 26.5 GB |
| F16 | 16.1 GB | Original precision | 17.4 GB | 17.9 GB | 19.0 GB | 21.2 GB | 25.5 GB | 34.0 GB |
Ollama ollama run hf.co/mradermacher/Llama-3.1-Swallow-8B-Instruct-v0.5-GGUF:Q4_K_M
llama.cpp llama-server -hf mradermacher/Llama-3.1-Swallow-8B-Instruct-v0.5-GGUF:Q4_K_M
GGUF source
tensorblock/tokyotech-llm_Llama-3.1-Swallow-8B-Instruct-v0.5-GGUF
Converted and published by a third party About this label
Last checked: Nov 11, 2026
- Architecture
- llama
- Supported before llama.cpp b4409
- Parameters
- 8.0B
- Max context length
- 131,072
- License
- llama3.3 Conditional
| Quantization | File size Measured | Quality guide | 4,096 | 8,192 | 16,384 | 32,768 | 65,536 | 131,072 max |
|---|---|---|---|---|---|---|---|---|
| Q2_K | 3.18 GB | Very large | 4.52 GB | 5.05 GB | 6.13 GB | 8.27 GB | 12.6 GB | 21.2 GB |
| Q3_K_M | 4.02 GB | Medium | 5.36 GB | 5.89 GB | 6.97 GB | 9.11 GB | 13.4 GB | 22.0 GB |
Ollama ollama run hf.co/tensorblock/tokyotech-llm_Llama-3.1-Swallow-8B-Instruct-v0.5-GGUF:Q2_K
llama.cpp llama-server -hf tensorblock/tokyotech-llm_Llama-3.1-Swallow-8B-Instruct-v0.5-GGUF:Q2_K
GGUF source
mradermacher/Llama-3.1-Swallow-8B-Instruct-v0.5-i1-GGUF
Converted and published by a third party About this label
Last checked: Nov 11, 2026
- Architecture
- llama
- Supported before llama.cpp b4409
- Parameters
- 8.0B
- Max context length
- 131,072
- License
- llama3.3 Conditional
Ollama ollama run hf.co/mradermacher/Llama-3.1-Swallow-8B-Instruct-v0.5-i1-GGUF:Q4_K_M
llama.cpp llama-server -hf mradermacher/Llama-3.1-Swallow-8B-Instruct-v0.5-i1-GGUF:Q4_K_M
Generation speed on common GPUs and Macs (estimated)
A rough guide to how fast Q4_K_M (4.92 GB) generates text. Each token reads the model weights once, so the upper limit is memory bandwidth ÷ file size. The guide is 60–80% of that, where most published measurements fall.
| Device | Memory bandwidth | Guide (tokens/s) Estimated | Upper limit |
|---|---|---|---|
| Arc A550M 8 GB | 224 GB/s | 27–36 | 46 |
| Radeon RX 6800 16 GB | 512 GB/s | 62–83 | 104 |
| GeForce RTX 5090 32 GB | 1,792 GB/s | 219–291 | 364 |
| Apple M2 16 GB | 100 GB/s | 12–16 | 20 |
| Apple M2 Pro 16 GB | 200 GB/s | 24–33 | 41 |
| Apple M5 Ultra 96 GB | 1,200 GB/s | 146–195 | 244 |
Estimates only. Prompt processing speed (limited by compute) is not included. Longer contexts and offloading part of the model to the CPU make it slower. Macs are shown only when installed memory is at least 4/3 of what is needed. Devices without published bandwidth (some GeForce cards, etc.) are not shown.
License and commercial use
Llama 3.3 Community License Conditional
Allows commercial use. Over 700 million monthly active users requires a separate license from Meta. Redistribution and published derivatives must show "Built with Llama", and you must follow the Acceptable Use Policy.
Based on the license on the original model's Hugging Face page. Original license text (checked Oct 18, 2026)
The summary states facts this site read from the original text and is not legal advice. Always check the original license and its latest terms before use. All licenses
How to run
Example commands using Q4_K_M. To use another quantization, replace the name with one from the table above. Commands for files from other sources are in each source's section. How to run local LLMs
Ollama
Run it by pointing directly at the GGUF on Hugging Face.
ollama run hf.co/mmnga/Llama-3.1-Swallow-8B-Instruct-v0.5-gguf:Q4_K_M
llama.cpp
Pass the repository and quantization name to llama-server (or llama-cli) with -hf.
llama-server -hf mmnga/Llama-3.1-Swallow-8B-Instruct-v0.5-gguf:Q4_K_M
LM Studio
Paste mmnga/Llama-3.1-Swallow-8B-Instruct-v0.5-gguf into the search box, download it, and choose the quantization from the file list.
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Measured Estimated VRAM 8 GB+
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Frequently asked questions
- How much memory do I need to run Llama-3.1-Swallow-8B-Instruct-v0.5 (Q4_K_M)?
- The file size is 4.92 GB (measured). With a 8,192-token context, the estimated memory including the KV cache is 6.79 GB. The formula is published at https://localllm.az-arte.com/en/guide/memory-calculation.
- Can Llama-3.1-Swallow-8B-Instruct-v0.5 be used commercially?
- The original model's license is Llama 3.3 Community License (Commercial use with conditions).Allows commercial use. Over 700 million monthly active users requires a separate license from Meta. Redistribution and published derivatives must show "Built with Llama", and you must follow the Acceptable Use Policy. Check the original license text before deciding.
- Does Llama-3.1-Swallow-8B-Instruct-v0.5 run in 8 GB, 12 GB, 16 GB or 24 GB of VRAM?
- 8 GB: up to Q6_K (est. 8.47 GB); 12 GB: up to Q8_0 (est. 10.4 GB); 16 GB: up to Q8_0 (est. 10.4 GB); 24 GB: up to Q8_0 (est. 10.4 GB) (estimated at 8,192 tokens with an f16 KV cache, files from the most-downloaded source).
- What is the command to run Llama-3.1-Swallow-8B-Instruct-v0.5 with Ollama?
- Run "ollama run hf.co/mmnga/Llama-3.1-Swallow-8B-Instruct-v0.5-gguf:Q4_K_M" to use the GGUF on Hugging Face directly. To use another quantization, replace the name at the end.