SAVRN Model Hub · Comparisons
DeepSeek-R1-0528-Qwen3-8B vs Llama-3.1-8B-Instruct
DeepSeek-R1-0528-Qwen3-8B has 8.2B parameters and Llama-3.1-8B-Instruct has 8B parameters; DeepSeek-R1-0528-Qwen3-8B is released under MIT License and Llama-3.1-8B-Instruct under Meta Llama 3.1 Community License; at 16-bit, DeepSeek-R1-0528-Qwen3-8B needs about 19.7 GB (1x MI300X from $1.85 an hour) and Llama-3.1-8B-Instruct about 19.3 GB (1x MI300X from $1.85 an hour).
| Field | DeepSeek-R1-0528-Qwen3-8B deepseek-ai/DeepSeek-R1-0528-Qwen3-8B | Llama-3.1-8B-Instruct meta-llama/Llama-3.1-8B-Instruct |
|---|---|---|
| Publisher | DeepSeek | Meta Llama |
| Task | Text generation | Text generation |
| Modality | Text | Text |
| Parameters, as reported | 8.2B parameters | 8B parameters |
| Architecture | Qwen3ForCausalLM | LlamaForCausalLM |
| Library | transformers | transformers |
| Context length | 131,072 tokens | Not stated |
| Repository size | 16.4 GB | 32.1 GB |
| Artifact formats | safetensors | safetensors, pytorch |
| License | mit | llama3.1 |
| Access | Open weights, no gate | Access requested at publisher |
| Memory at 16-bit (weights and margin) | 19.7 GB | 19.3 GB |
| Cheapest GPUs at 16-bit, per hour | 1x MI300X, $1.85 | 1x MI300X, $1.85 |
| Memory at 4-bit (weights and margin) | 4.9 GB | 4.8 GB |
| Cheapest GPUs at 4-bit, per hour | 1x MI300X, $1.85 | 1x MI300X, $1.85 |
| Revision viewed | 6e8885a6ff5c | 0e9e39f249a1 |
| Downloads reported by the hub | 895.6k | 5.9M |
| Last observed | 2026-09-18 | 2026-09-18 |
An evaluation row appears only where at least two of these models report the same benchmark with the same stated configuration, metric, unit and setup. Different evaluators stay named in each cell. Values are shown as reported: no unit conversion, no ranking.
Other Reported Results
These results are listed for each model on its own, because the conditions needed to compare them are not stated or do not match. Two results that leave a condition blank are not assumed to share it.
Llama-3.1-8B-Instruct
| Benchmark | Conditions | Result | Reported by | Revision | Date |
|---|---|---|---|---|---|
| Idavidrein/gpqa | Task diamondMetric diamondComparison conditions not established | 30.4 | Model Card Reported by a third party |
Evaluated revision not stated | 2026-01-27 |
| LEXam-Benchmark/LEXam | Task mcq_4_choicesMetric mcq_4_choicesComparison conditions not established | 24.04 | LEXam Leaderboard Reported by a third party |
Evaluated revision not stated | 2026-06-02 |
| LEXam-Benchmark/LEXam | Task open_questionMetric open_questionComparison conditions not established | 10 | LEXam Leaderboard Reported by a third party |
Evaluated revision not stated | 2026-06-02 |
| openai/gsm8k | Task gsm8kMetric gsm8kComparison conditions not established | 84.5 | Model Card Reported by a third party |
Evaluated revision not stated | 2026-03-23 |
| thamilvendhan/signalbench | Task access_denyMetric access_denySetup family=access_deny; n=12Comparison conditions not established | 0.5 | thamilvendhan Reported by a third party |
Evaluated revision not stated | 2026-07-08 |
| thamilvendhan/signalbench | Task bot_policyMetric bot_policySetup family=bot_policy; n=12Comparison conditions not established | 0.4167 | thamilvendhan Reported by a third party |
Evaluated revision not stated | 2026-07-08 |
| thamilvendhan/signalbench | Task injectionMetric injectionSetup family=injection; n=12Comparison conditions not established | 0.75 | thamilvendhan Reported by a third party |
Evaluated revision not stated | 2026-07-08 |
| thamilvendhan/signalbench | Task memory_labelMetric memory_labelSetup family=memory_label; n=12Comparison conditions not established | 0.6667 | thamilvendhan Reported by a third party |
Evaluated revision not stated | 2026-07-08 |
| thamilvendhan/signalbench | Task srcMetric srcSetup SRC overall; deterministic action-based grader, no LLM judge; seed 0, n=75Comparison conditions not established | 0.6333 | signalbench raw per-item responses Reported by a third party |
Evaluated revision not stated | 2026-07-08 |
| thamilvendhan/signalbench | Task timeMetric timeSetup family=time; n=12Comparison conditions not established | 0.8333 | thamilvendhan Reported by a third party |
Evaluated revision not stated | 2026-07-08 |
SAVRN's Notes on DeepSeek-R1-0528-Qwen3-8B
At 16-bit precision this model asks for 19.7 GB of memory, which settles the hardware question early. DeepSeek built it on the Qwen3ForCausalLM architecture at 8.2 billion parameters for text generation, with a 131,072-token context window and weights that occupy 16.4 GB as safetensors. Drop to 8-bit and the memory need falls to 9.8 GB; at 4-bit it is 4.9 GB. The cheapest setup on our Index is a single MI300X with 192 GB at $1.85 per hour on demand, so one card holds it many times over.
The MIT license permits commercial use, modification and redistribution as long as the copyright and permission notices stay with the files. Before committing, confirm your workload needs the full 131,072-token context, read the describing paper arXiv:2501.12948, and note that our Index lists no per-token host prices for this model yet, so the hourly card rate is the only cost reference.
SAVRN's Notes on Llama-3.1-8B-Instruct
At 16-bit this checkpoint needs 19.3 GB of memory, and the cheapest SAVRN Index listing that covers it is a single 192 GB MI300X at $1.85 per hour on-demand. That card is the cheapest answer at 8-bit (9.6 GB) and 4-bit (4.8 GB) too, so quantizing does not buy a cheaper hour; it buys room for more concurrent multilingual dialogue sessions on one card.
Commercial use is allowed under the Meta Llama 3.1 Community License, with attribution and Meta's Acceptable Use Policy observed, unless your products had more than 700 million monthly active users on the release date; then you request a license from Meta. Gated access means approval precedes download. Two checks: the page lists no context length, and the $1.85 hour must beat the Index's hosted rates, $0.02 in and $0.05 out per million tokens at DeepInfra and Novita, $0.06 both ways at Nscale.
Questions
Which is larger, DeepSeek-R1-0528-Qwen3-8B or Llama-3.1-8B-Instruct?
DeepSeek-R1-0528-Qwen3-8B (8.2B parameters) is larger than Llama-3.1-8B-Instruct (8B parameters), by the parameter counts their publishers report.
Which is cheaper to run, DeepSeek-R1-0528-Qwen3-8B or Llama-3.1-8B-Instruct?
At 4-bit, DeepSeek-R1-0528-Qwen3-8B fits on 1x MI300X from $1.85 an hour and Llama-3.1-8B-Instruct on 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.
Can I use DeepSeek-R1-0528-Qwen3-8B commercially?
Yes. DeepSeek-R1-0528-Qwen3-8B is released under MIT License. The MIT License is a short permissive license. It permits commercial use, modification and redistribution, provided the copyright notice and permission notice are included.
Can I use Llama-3.1-8B-Instruct commercially?
Yes, with conditions. Llama-3.1-8B-Instruct is released under Meta Llama 3.1 Community License. The Llama 3.1 Community License permits commercial use, except that a licensee whose products had more than 700 million monthly active users on the release date must request a license from Meta. It requires attribution as the license specifies and compliance with Meta's Acceptable Use Policy.