Meta Llama 3 8B Instruct vs Qwen3.8 27B
Qwen3.8 27B leads llmrun Score 121.5 to 61.7; both fit 24 GB at Q4_K_M; Meta Llama 3 8B Instruct is 3.3× faster on RTX 4090 (~124 vs ~38 tok/s).
Model B · Alibaba
Qwen3.8 27B27.8B · 256K context · Apache 2.0 · Aug 2026
Choose Meta Llama 3 8B Instruct if you want…
Fits an 8 GB GPU
Q4_K_M · 7.9 GB; Qwen3.8 27B needs 8.8 GB at its smallest quant
3.3× faster on RTX 4090
~124 vs ~38 tok/s
Lighter on memory
5.3 vs 17.4 GB at Q4_K_M
Also does chat
Qwen3.8 27B doesn't list it
Choose Qwen3.8 27B if you want…
Higher llmrun Score
121.5 vs 61.7
Stronger at reasoning
124.1 vs 63.9
Also does vision, function-calling
Meta Llama 3 8B Instruct doesn't list it
More permissive license
Apache 2.0 vs Llama 3 Community
Newer release
Aug 2026 vs Apr 2024
On your hardware
Each model at its default quant (Q4_K_M), a smaller one when it must. Estimates, ±20%.
8 GB
RTX 4060 · 8 GB tier
Meta Llama 3 8B Instruct
Q4_K_M · ~33 tok/s
7.9 GB · up to 16Kfits
Qwen3.8 27B
Doesn't fit
smallest quant needs 8.8 GB
12 GB
RTX 3060 12GB · 12 GB tier
Meta Llama 3 8B Instruct
Q4_K_M · ~44 tok/s
7.9 GB · up to 32Kmore headroom
Qwen3.8 27B
IQ3_XXS · ~20 tok/s
12 GB · up to 16K
16 GB
RTX 5060 Ti 16GB · 16 GB tier
Meta Llama 3 8B Instruct
Q4_K_M · ~55 tok/s
7.9 GB · up to 32Kmore headroom
Qwen3.8 27B
Q3_K_L · ~19 tok/s
15.4 GB · up to 16K
24 GBreference
RTX 4090 · 24 GB tier
Meta Llama 3 8B Instruct
Q4_K_M · ~124 tok/s
7.9 GB · up to 32Kfaster
Qwen3.8 27B
Q4_K_M · ~38 tok/s
17.9 GB · up to 64K
32 GB
RTX 5090 · 32 GB tier
Meta Llama 3 8B Instruct
Q4_K_M · ~220 tok/s
7.9 GB · up to 32Kfaster
Qwen3.8 27B
Q4_K_M · ~67 tok/s
17.9 GB · up to 256K
48 GB
RTX A6000 · 48 GB tier
Meta Llama 3 8B Instruct
Q4_K_M · ~94 tok/s
7.9 GB · up to 32Kfaster
Qwen3.8 27B
Q4_K_M · ~29 tok/s
17.9 GB · up to 256K
Mac 32 GB
Mac Mini M4 · Macs
Meta Llama 3 8B Instruct
Q4_K_M · ~16 tok/s
7.9 GB · up to 32Kfaster
Qwen3.8 27B
Q4_K_M · ~5 tok/s
17.9 GB · up to 128K
Mac 64 GB
Mac Studio M4 Max · Macs
Meta Llama 3 8B Instruct
Q4_K_M · ~72 tok/s
7.9 GB · up to 32Kfaster
Qwen3.8 27B
Q4_K_M · ~22 tok/s
17.9 GB · up to 256K
Mac 128 GB
Mac Studio M4 Max · Macs
Meta Llama 3 8B Instruct
Q4_K_M · ~72 tok/s
7.9 GB · up to 32Kfaster
Qwen3.8 27B
Q4_K_M · ~22 tok/s
17.9 GB · up to 256K
Reference GPU: RTX 4090. Tinted cell = the better pick on that machine (fits, more headroom, or at least 10% faster).
Overall
Meta Llama 3 8B Instruct
Qwen3.8 27B
Coding
Meta Llama 3 8B Instruct
no data
Qwen3.8 27B
Math
Meta Llama 3 8B Instruct
no data
Qwen3.8 27B
Science
Meta Llama 3 8B Instruct
no data
Qwen3.8 27B
Reasoning
Meta Llama 3 8B Instruct
Qwen3.8 27B
Meta Llama 3 8B Instruct · #89 of 92 open models · #32 of 34 that fit 24 GB
Qwen3.8 27B · #10 of 92 open models · #1 of 34 that fit 24 GB
Benchmarks
2 shared boards: Meta Llama 3 8B Instruct leads 0, Qwen3.8 27B leads 2 · Benchmarks imported Oct 2026
Meta Llama 3 8B Instruct
1223.6#136/186 open
ArenaLMArena · Oct 2026
Qwen3.8 27B
1438.1#22/186 open
ArenaLMArena · Oct 2026
Meta Llama 3 8B Instruct
43.9%#66/68 open
Lab-reportedEpoch AI · Apr 2024
Qwen3.8 27B
88.0%#9/68 open
Lab-reportedEpoch AI · Aug 2026
23 more boards (other shared and single-model boards)
Only Meta Llama 3 8B Instruct (11)
- GPQA Diamond26.1%
- AIME 2024/20251.9%
- MMLU-Pro41.0%
- MATH Level 56.1%
- MMLU68.8%
- Chess Puzzles0.0%
- ForecastBench58.6
- WinoGrande65.0%
- ARC Challenge82.8%
- OpenBookQA82.6%
- TriviaQA67.7%
Only Qwen3.8 27B (12)
- LiveBench Coding75.7
- LiveBench Reasoning80.0
- LMArena WebDev1592.8
- LiveBench Math86.2
- ARC-AGI-242.4%
- SciCode46.6%
- ARC-AGI87.5%
- CritPt5.4%
- LMCA41.4%
- APEX-Agents47.5%
- Surface Evolver Bench45.0%
- ProofBench16.0%
VRAM by quantization
Memory to load the model and 2K context; below it, the total at 32K and 128K context. Lighter side in bold.
Q2_K
Meta Llama 3 8B Instruct
3.8 GB3.2 GB file
32K 9.3 GB · 128K 26.9 GB
Qwen3.8 27B
12.6 GB10.8 GB file
32K 13.9 GB · 128K 19.3 GB
Q3_K_S
Meta Llama 3 8B Instruct
3.9 GB3.7 GB file
32K 9.4 GB · 128K 27.0 GB
Qwen3.8 27B
12.9 GB12.7 GB file
32K 14.2 GB · 128K 19.6 GB
Q3_K_M
Meta Llama 3 8B Instruct
4.3 GB4.0 GB file
32K 9.8 GB · 128K 27.4 GB
Qwen3.8 27B
14.3 GB13.4 GB file
32K 15.6 GB · 128K 21.0 GB
Q4_0
Meta Llama 3 8B Instruct
4.4 GB4.7 GB file
32K 9.9 GB · 128K 27.5 GB
Qwen3.8 27B
14.6 GB~13.9 GB file
32K 16.0 GB · 128K 21.4 GB
Q4_K_M
Meta Llama 3 8B Instruct
5.3 GB4.9 GB file
32K 10.8 GB · 128K 28.4 GB
Qwen3.8 27B
17.4 GB16.5 GB file
32K 18.8 GB · 128K 24.1 GB
Q5_K_M
Meta Llama 3 8B Instruct
6.3 GB5.7 GB file
32K 11.8 GB · 128K 29.4 GB
Qwen3.8 27B
20.5 GB19.8 GB file
32K 21.9 GB · 128K 27.3 GB
Q6_K
Meta Llama 3 8B Instruct
7.3 GB6.6 GB file
32K 12.8 GB · 128K 30.4 GB
Qwen3.8 27B
23.7 GB22.0 GB file
32K 25.0 GB · 128K 30.4 GB
Q8_0
Meta Llama 3 8B Instruct
8.8 GB8.5 GB file
32K 14.3 GB · 128K 32.0 GB
Qwen3.8 27B
28.5 GB29.0 GB file
32K 29.9 GB · 128K 35.2 GB
20 more quants
IQ2_XXS
Meta Llama 3 8B Instruct
2.4 GB2.4 GB file
32K 7.9 GB · 128K 25.6 GB
Qwen3.8 27B
8.4 GB7.3 GB file
32K 9.7 GB · 128K 15.1 GB
IQ2_XS
Meta Llama 3 8B Instruct
2.7 GB2.6 GB file
32K 8.2 GB · 128K 25.8 GB
Qwen3.8 27B
9.1 GB9.1 GB file
32K 10.4 GB · 128K 15.8 GB
IQ2_S
Meta Llama 3 8B Instruct
2.8 GB2.8 GB file
32K 8.3 GB · 128K 25.9 GB
Qwen3.8 27B
9.4 GB8.4 GB file
32K 10.8 GB · 128K 16.1 GB
IQ2_M
Meta Llama 3 8B Instruct
3.0 GB2.9 GB file
32K 8.5 GB · 128K 26.1 GB
Qwen3.8 27B
10.1 GB10.5 GB file
32K 11.5 GB · 128K 16.8 GB
IQ3_XXS
Meta Llama 3 8B Instruct
3.4 GB3.3 GB file
32K 8.9 GB · 128K 26.5 GB
Qwen3.8 27B
11.5 GB10.9 GB file
32K 12.9 GB · 128K 18.2 GB
Q2_K_S
Meta Llama 3 8B Instruct
3.5 GB3.0 GB file
32K 9.0 GB · 128K 26.7 GB
Qwen3.8 27B
not listed
IQ3_XS
Meta Llama 3 8B Instruct
3.6 GB3.5 GB file
32K 9.1 GB · 128K 26.8 GB
Qwen3.8 27B
12.2 GB10.4 GB file
32K 13.6 GB · 128K 18.9 GB
IQ3_S
Meta Llama 3 8B Instruct
3.8 GB3.7 GB file
32K 9.3 GB · 128K 26.9 GB
Qwen3.8 27B
12.6 GB12.0 GB file
32K 13.9 GB · 128K 19.3 GB
IQ3_M
Meta Llama 3 8B Instruct
4.0 GB3.8 GB file
32K 9.5 GB · 128K 27.1 GB
Qwen3.8 27B
13.3 GB14.9 GB file
32K 14.6 GB · 128K 20.0 GB
Q3_K_L
Meta Llama 3 8B Instruct
4.5 GB4.3 GB file
32K 10.0 GB · 128K 27.7 GB
Qwen3.8 27B
15.0 GB14.1 GB file
32K 16.3 GB · 128K 21.7 GB
IQ4_XS
Meta Llama 3 8B Instruct
4.8 GB4.4 GB file
32K 10.3 GB · 128K 27.9 GB
Qwen3.8 27B
15.7 GB14.3 GB file
32K 17.0 GB · 128K 22.4 GB
IQ4_NL
Meta Llama 3 8B Instruct
5.0 GB4.7 GB file
32K 10.5 GB · 128K 28.1 GB
Qwen3.8 27B
16.4 GB17.4 GB file
32K 17.7 GB · 128K 23.1 GB
Q4_1
Meta Llama 3 8B Instruct
5.0 GB5.1 GB file
32K 10.5 GB · 128K 28.1 GB
Qwen3.8 27B
16.4 GB17.5 GB file
32K 17.7 GB · 128K 23.1 GB
Q4_K_S
Meta Llama 3 8B Instruct
5.0 GB4.7 GB file
32K 10.5 GB · 128K 28.1 GB
Qwen3.8 27B
16.4 GB15.4 GB file
32K 17.7 GB · 128K 23.1 GB
Q4_K_L
Meta Llama 3 8B Instruct
not listed
Qwen3.8 27B
17.8 GB18.8 GB file
32K 19.1 GB · 128K 24.5 GB
Q5_0
Meta Llama 3 8B Instruct
5.5 GB5.6 GB file
32K 11.0 GB · 128K 28.6 GB
Qwen3.8 27B
not listed
Q5_1
Meta Llama 3 8B Instruct
6.1 GB6.1 GB file
32K 11.6 GB · 128K 29.2 GB
Qwen3.8 27B
not listed
Q5_K_S
Meta Llama 3 8B Instruct
6.1 GB5.6 GB file
32K 11.6 GB · 128K 29.2 GB
Qwen3.8 27B
19.9 GB18.7 GB file
32K 21.2 GB · 128K 26.6 GB
Q5_K_L
Meta Llama 3 8B Instruct
not listed
Qwen3.8 27B
20.9 GB21.5 GB file
32K 22.2 GB · 128K 27.6 GB
BF16
Meta Llama 3 8B Instruct
17.7 GB~16.1 GB file
32K 23.2 GB · 128K 40.8 GB
Qwen3.8 27B
56.3 GB54.7 GB file
32K 57.7 GB · 128K 63.0 GB
Meta Llama 3 8B Instruct
Ollama · Q4_K_M
ollama run hf.co/MaziyarPanahi/Meta-Llama-3-8B-Instruct-GGUF:Q4_K_MQwen3.8 27B
Ollama · Q4_K_M
ollama run hf.co/unsloth/Qwen3.8-27B-GGUF:Q4_K_M| Spec | Meta Llama 3 8B Instruct | Qwen3.8 27B |
|---|---|---|
| Publisher | Meta | Alibaba |
| Parameters | 8.0B | 27.8B |
| Architecture | — | Qwen3_5ForConditionalGeneration |
| Context | — | 256K tokens |
| Modalities | Chat | Vision, Function-calling |
| License | Llama 3 Community | Apache 2.0 |
| Released | Apr 2024 | Aug 2026 |
Frequently Asked Questions
Which is better, Meta Llama 3 8B Instruct or Qwen3.8 27B?
Qwen3.8 27B leads llmrun Score 121.5 to 61.7; both fit 24 GB at Q4_K_M; Meta Llama 3 8B Instruct is 3.3× faster on RTX 4090 (~124 vs ~38 tok/s). Choose Meta Llama 3 8B Instruct for fits an 8 GB GPU and 3.3× faster on RTX 4090; choose Qwen3.8 27B for higher llmrun Score and stronger at reasoning.
Which needs less VRAM, Meta Llama 3 8B Instruct or Qwen3.8 27B?
At Q4_K_M, Meta Llama 3 8B Instruct requires 5.3 GB while Qwen3.8 27B needs 17.4 GB, making Meta Llama 3 8B Instruct the easier model to run on your local setup.
Which is faster on RTX 4090?
Estimated decode speed on RTX 4090 is about 124 tok/s for Meta Llama 3 8B Instruct (Q4_K_M) and 38 tok/s for Qwen3.8 27B (Q4_K_M), so Meta Llama 3 8B Instruct is faster. Estimates, ±20%.
Can I run Meta Llama 3 8B Instruct or Qwen3.8 27B on a 16 GB GPU?
Meta Llama 3 8B Instruct fits at Q4_K_M; Qwen3.8 27B fits at Q3_K_L.