SAVRN Model Hub · Comparisons
Llama-3.1-8B-Instruct vs Qwen3-8B
Llama-3.1-8B-Instruct has 8B parameters and Qwen3-8B has 8.2B parameters; Llama-3.1-8B-Instruct is released under Meta Llama 3.1 Community License and Qwen3-8B under Apache License 2.0; at 16-bit, Llama-3.1-8B-Instruct needs about 19.3 GB (1x MI300X from $1.85 an hour) and Qwen3-8B about 19.7 GB (1x MI300X from $1.85 an hour).
| Field | Llama-3.1-8B-Instruct meta-llama/Llama-3.1-8B-Instruct | Qwen3-8B Qwen/Qwen3-8B |
|---|---|---|
| Publisher | Meta Llama | Qwen |
| Task | Text generation | Text generation |
| Modality | Text | Text |
| Parameters, as reported | 8B parameters | 8.2B parameters |
| Architecture | LlamaForCausalLM | Qwen3ForCausalLM |
| Library | transformers | transformers |
| Context length | Not stated | 40,960 tokens |
| Repository size | 32.1 GB | 16.4 GB |
| Artifact formats | safetensors, pytorch | safetensors |
| License | llama3.1 | apache-2.0 |
| Access | Access requested at publisher | Open weights, no gate |
| Memory at 16-bit (weights and margin) | 19.3 GB | 19.7 GB |
| Cheapest GPUs at 16-bit, per hour | 1x MI300X, $1.85 | 1x MI300X, $1.85 |
| Memory at 4-bit (weights and margin) | 4.8 GB | 4.9 GB |
| Cheapest GPUs at 4-bit, per hour | 1x MI300X, $1.85 | 1x MI300X, $1.85 |
| Revision viewed | 0e9e39f249a1 | b968826d9c46 |
| Downloads reported by the hub | 5.9M | 13M |
| 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 |
Qwen3-8B
| Benchmark | Conditions | Result | Reported by | Revision | Date |
|---|---|---|---|---|---|
| LiquidAI/ifstruct-v1.0 | Task ifstruct_v1Metric ifstruct_v1Comparison conditions not established | 79.75 | Liquid AI — IFStruct v1.0 blog (Qwen3-8B) Reported by a third party |
Evaluated revision not stated | 2026-06-30 |
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.
SAVRN's Notes on Qwen3-8B
At 16-bit the weights are 16.4 GB and the model needs 19.7 GB, which fits one MI300X with 192 GB, the Index's cheapest setup at $1.85 an hour on-demand. At 8-bit the need drops to 9.8 GB and at 4-bit to 4.9 GB, so the question is never which card but how many copies to stack on one. With 8.2 billion parameters, a 40,960-token context and a switch between thinking and non-thinking modes, this is a text model for everyday work.
The license is the easy part: Apache 2.0 permits commercial use, modification and redistribution, with notices and any NOTICE file kept and significant changes stated, plus an express patent grant. Two things to weigh: it derives from Qwen3-8B-Base, so decide whether you want this checkpoint or the base, and compare the hourly card against the one Index host price, Nscale at $0.07 in and $0.18 out per million tokens.
Questions
Which is larger, Llama-3.1-8B-Instruct or Qwen3-8B?
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, Llama-3.1-8B-Instruct or Qwen3-8B?
At 4-bit, Llama-3.1-8B-Instruct fits on 1x MI300X from $1.85 an hour and Qwen3-8B on 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.
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.
Can I use Qwen3-8B commercially?
Yes. Qwen3-8B is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.