Llama 3.1 70B Instruct vs Qwen3.8 27B
Qwen3.8 27B leads llmrun Score 122.1 to 77.9; only Qwen3.8 27B fits 24 GB (Q4_K_M); Llama 3.1 70B Instruct needs 26.1 GB even at its smallest quant.
Model A · Meta
Llama 3.1 70B Instruct70.6B · 128K context · Llama 3.1 Community · Jul 2024
Model B · Alibaba
Qwen3.8 27B27.8B · 256K context · Apache 2.0 · Aug 2026
Choose Llama 3.1 70B Instruct if you want…
Also does chat
Qwen3.8 27B doesn't list it
Choose Qwen3.8 27B if you want…
Higher llmrun Score
122.1 vs 77.9
Stronger at reasoning
124.0 vs 83.2
Fits a 12 GB GPU
IQ3_XXS · 12 GB; Llama 3.1 70B Instruct needs 26.1 GB at its smallest quant
Fits a Mac 32 GB
Q4_K_M · 17.9 GB; Llama 3.1 70B Instruct needs 26.1 GB at its smallest quant
Lighter on memory
17.4 vs 46.6 GB at Q4_K_M
Longer context window
256K vs 128K tokens
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
Llama 3.1 70B Instruct
Doesn't fit
smallest quant needs 26.1 GB
Qwen3.8 27B
Doesn't fit
smallest quant needs 8.8 GB
12 GB
RTX 3060 12GB · 12 GB tier
Llama 3.1 70B Instruct
Doesn't fit
smallest quant needs 26.1 GB
Qwen3.8 27B
IQ3_XXS · ~20 tok/s
12 GB · up to 16Kfits
16 GB
RTX 5060 Ti 16GB · 16 GB tier
Llama 3.1 70B Instruct
Doesn't fit
smallest quant needs 26.1 GB
Qwen3.8 27B
Q3_K_L · ~19 tok/s
15.4 GB · up to 16Kfits
24 GBreference
RTX 4090 · 24 GB tier
Llama 3.1 70B Instruct
Doesn't fit
smallest quant needs 26.1 GB
Qwen3.8 27B
Q4_K_M · ~38 tok/s
17.9 GB · up to 64Kfits
32 GB
RTX 5090 · 32 GB tier
Llama 3.1 70B Instruct
IQ2_M · ~45 tok/s
30.9 GB · up to 16K
Qwen3.8 27B
Q4_K_M · ~67 tok/s
17.9 GB · up to 256Kmore headroom
48 GB
RTX A6000 · 48 GB tier
Llama 3.1 70B Instruct
Q4_K_M · ~11 tok/stight
51.3 GB · up to 4K
Qwen3.8 27B
Q4_K_M · ~29 tok/s
17.9 GB · up to 256Kmore headroom
Mac 32 GB
Mac Mini M4 · Macs
Llama 3.1 70B Instruct
IQ2_XS · ~4 tok/s
28 GB · up to 16K
Qwen3.8 27B
Q4_K_M · ~5 tok/s
17.9 GB · up to 128Kmore headroom
Mac 64 GB
Mac Studio M4 Max · Macs
Llama 3.1 70B Instruct
Q4_K_M · ~8 tok/s
51.3 GB · up to 32K
Qwen3.8 27B
Q4_K_M · ~22 tok/s
17.9 GB · up to 256Kfaster
Mac 128 GB
Mac Studio M4 Max · Macs
Llama 3.1 70B Instruct
Q4_K_M · ~8 tok/s
51.3 GB · up to 128K
Qwen3.8 27B
Q4_K_M · ~22 tok/s
17.9 GB · up to 256Kfaster
Reference GPU: RTX 4090. Tinted cell = the better pick on that machine (fits, more headroom, or at least 10% faster).
Overall
Llama 3.1 70B Instruct
Qwen3.8 27B
Coding
Llama 3.1 70B Instruct
no data
Qwen3.8 27B
Agents
Llama 3.1 70B Instruct
no data
Qwen3.8 27B
Math
Llama 3.1 70B Instruct
no data
Qwen3.8 27B
Science
Llama 3.1 70B Instruct
no data
Qwen3.8 27B
Reasoning
Llama 3.1 70B Instruct
Qwen3.8 27B
Llama 3.1 70B Instruct · #87 of 100 open models · #37 of 49 that fit ≤64 GB
Qwen3.8 27B · #11 of 100 open models · #1 of 35 that fit 24 GB
Benchmarks
2 shared boards: Llama 3.1 70B Instruct leads 0, Qwen3.8 27B leads 2 · Benchmarks imported Oct 2026
26 more boards (other shared and single-model boards)
Llama 3.1 70B Instruct
1293.4#109/187 open
ArenaLMArena · Oct 2026
Qwen3.8 27B
1437.8#22/187 open
ArenaLMArena · Oct 2026
Only Llama 3.1 70B Instruct (7)
- GPQA Diamond44.2%
- AIME 2024/20253.6%
- MMLU-Pro62.8%
- MATH Level 536.7%
- MMLU80.1%
- WeirdML9.0%
- BALROG27.9%
Only Qwen3.8 27B (18)
- LiveBench Coding75.7
- LiveBench Reasoning80.0
- LMArena WebDev1593.2
- LiveBench Math86.2
- ARC-AGI-242.4%
- SciCode46.6%
- ARC-AGI87.5%
- APEX-Agents47.5%
- CritPt5.4%
- LiveBench 2026 Reasoning80.0
- LiveBench 2026 Browser32.0
- LiveBench 2026 Data Analysis76.6
- LiveBench 2026 Coding86.8
- LiveBench 2026 Long-Horizon27.9
- Surface Evolver Bench45.0%
- LiveBench 2026 Design75.9
- ProofBench16.0%
- LiveBench 2026 Research59.4
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
Llama 3.1 70B Instruct
33.0 GB26.4 GB file
32K 43.1 GB · 128K 75.5 GB
Qwen3.8 27B
12.6 GB10.8 GB file
32K 13.9 GB · 128K 19.3 GB
Q3_K_S
Llama 3.1 70B Instruct
34.0 GB30.9 GB file
32K 44.1 GB · 128K 76.4 GB
Qwen3.8 27B
12.9 GB12.7 GB file
32K 14.2 GB · 128K 19.6 GB
Q3_K_M
Llama 3.1 70B Instruct
37.8 GB34.3 GB file
32K 48.0 GB · 128K 80.3 GB
Qwen3.8 27B
14.3 GB13.4 GB file
32K 15.6 GB · 128K 21.0 GB
Q4_0
Llama 3.1 70B Instruct
38.8 GB40.1 GB file
32K 48.9 GB · 128K 81.3 GB
Qwen3.8 27B
14.6 GB~13.9 GB file
32K 16.0 GB · 128K 21.4 GB
Q4_K_M
Llama 3.1 70B Instruct
46.6 GB42.5 GB file
32K 56.7 GB · 128K 89.1 GB
Qwen3.8 27B
17.4 GB16.5 GB file
32K 18.8 GB · 128K 24.1 GB
Q5_K_M
Llama 3.1 70B Instruct
55.3 GB49.9 GB file
32K 65.4 GB · 128K 97.8 GB
Qwen3.8 27B
20.5 GB19.8 GB file
32K 21.9 GB · 128K 27.3 GB
Q6_K
Llama 3.1 70B Instruct
64.0 GB57.9 GB file
32K 74.2 GB · 128K 107 GB
Qwen3.8 27B
23.7 GB22.0 GB file
32K 25.0 GB · 128K 30.4 GB
Q8_0
Llama 3.1 70B Instruct
77.6 GB75.0 GB file
32K 87.7 GB · 128K 120 GB
Qwen3.8 27B
28.5 GB29.0 GB file
32K 29.9 GB · 128K 35.2 GB
18 more quants
IQ2_XXS
Llama 3.1 70B Instruct
21.3 GB19.1 GB file
32K 31.5 GB · 128K 63.8 GB
Qwen3.8 27B
8.4 GB7.3 GB file
32K 9.7 GB · 128K 15.1 GB
IQ2_XS
Llama 3.1 70B Instruct
23.3 GB21.1 GB file
32K 33.4 GB · 128K 65.8 GB
Qwen3.8 27B
9.1 GB9.1 GB file
32K 10.4 GB · 128K 15.8 GB
IQ2_S
Llama 3.1 70B Instruct
24.3 GB22.2 GB file
32K 34.4 GB · 128K 66.7 GB
Qwen3.8 27B
9.4 GB8.4 GB file
32K 10.8 GB · 128K 16.1 GB
IQ2_M
Llama 3.1 70B Instruct
26.2 GB24.1 GB file
32K 36.3 GB · 128K 68.7 GB
Qwen3.8 27B
10.1 GB10.5 GB file
32K 11.5 GB · 128K 16.8 GB
IQ3_XXS
Llama 3.1 70B Instruct
30.1 GB27.5 GB file
32K 40.2 GB · 128K 72.6 GB
Qwen3.8 27B
11.5 GB10.9 GB file
32K 12.9 GB · 128K 18.2 GB
IQ3_XS
Llama 3.1 70B Instruct
32.0 GB29.3 GB file
32K 42.1 GB · 128K 74.5 GB
Qwen3.8 27B
12.2 GB10.4 GB file
32K 13.6 GB · 128K 18.9 GB
IQ3_S
Llama 3.1 70B Instruct
33.0 GB30.9 GB file
32K 43.1 GB · 128K 75.5 GB
Qwen3.8 27B
12.6 GB12.0 GB file
32K 13.9 GB · 128K 19.3 GB
IQ3_M
Llama 3.1 70B Instruct
34.9 GB31.9 GB file
32K 45.0 GB · 128K 77.4 GB
Qwen3.8 27B
13.3 GB14.9 GB file
32K 14.6 GB · 128K 20.0 GB
Q3_K_L
Llama 3.1 70B Instruct
39.8 GB37.1 GB file
32K 49.9 GB · 128K 82.3 GB
Qwen3.8 27B
15.0 GB14.1 GB file
32K 16.3 GB · 128K 21.7 GB
IQ4_XS
Llama 3.1 70B Instruct
41.7 GB37.9 GB file
32K 51.8 GB · 128K 84.2 GB
Qwen3.8 27B
15.7 GB14.3 GB file
32K 17.0 GB · 128K 22.4 GB
IQ4_NL
Llama 3.1 70B Instruct
not listed
Qwen3.8 27B
16.4 GB17.4 GB file
32K 17.7 GB · 128K 23.1 GB
Q4_1
Llama 3.1 70B Instruct
not listed
Qwen3.8 27B
16.4 GB17.5 GB file
32K 17.7 GB · 128K 23.1 GB
Q4_K_S
Llama 3.1 70B Instruct
43.7 GB40.3 GB file
32K 53.8 GB · 128K 86.2 GB
Qwen3.8 27B
16.4 GB15.4 GB file
32K 17.7 GB · 128K 23.1 GB
Q4_K_L
Llama 3.1 70B Instruct
47.5 GB43.3 GB file
32K 57.7 GB · 128K 90.0 GB
Qwen3.8 27B
17.8 GB18.8 GB file
32K 19.1 GB · 128K 24.5 GB
Q5_0
Llama 3.1 70B Instruct
48.5 GB48.7 GB file
32K 58.6 GB · 128K 91.0 GB
Qwen3.8 27B
not listed
Q5_K_S
Llama 3.1 70B Instruct
53.4 GB48.7 GB file
32K 63.5 GB · 128K 95.9 GB
Qwen3.8 27B
19.9 GB18.7 GB file
32K 21.2 GB · 128K 26.6 GB
Q5_K_L
Llama 3.1 70B Instruct
56.3 GB50.6 GB file
32K 66.4 GB · 128K 98.8 GB
Qwen3.8 27B
20.9 GB21.5 GB file
32K 22.2 GB · 128K 27.6 GB
BF16
Llama 3.1 70B Instruct
155 GB~141.1 GB file
32K 165 GB · 128K 198 GB
Qwen3.8 27B
56.3 GB54.7 GB file
32K 57.7 GB · 128K 63.0 GB
Llama 3.1 70B Instruct
Ollama · Q4_K_M
ollama run hf.co/MaziyarPanahi/Meta-Llama-3.1-70B-Instruct-GGUF:Q4_K_MQwen3.8 27B
Ollama · Q4_K_M
ollama run hf.co/unsloth/Qwen3.8-27B-GGUF:Q4_K_M| Spec | Llama 3.1 70B Instruct | Qwen3.8 27B |
|---|---|---|
| Publisher | Meta | Alibaba |
| Parameters | 70.6B | 27.8B |
| Architecture | — | Qwen3_5ForConditionalGeneration |
| Context | 128K tokens | 256K tokens |
| Modalities | Chat, Function-calling | Vision, Function-calling |
| License | Llama 3.1 Community | Apache 2.0 |
| Released | Jul 2024 | Aug 2026 |
Frequently Asked Questions
Which is better, Llama 3.1 70B Instruct or Qwen3.8 27B?
Qwen3.8 27B leads llmrun Score 122.1 to 77.9; only Qwen3.8 27B fits 24 GB (Q4_K_M); Llama 3.1 70B Instruct needs 26.1 GB even at its smallest quant. Choose Llama 3.1 70B Instruct for also does chat; choose Qwen3.8 27B for higher llmrun Score and stronger at reasoning.
Which needs less VRAM, Llama 3.1 70B Instruct or Qwen3.8 27B?
At Q4_K_M, Llama 3.1 70B Instruct requires 46.6 GB while Qwen3.8 27B needs 17.4 GB, making Qwen3.8 27B the easier model to run on your local setup.
Can I run Llama 3.1 70B Instruct or Qwen3.8 27B on a 16 GB GPU?
Llama 3.1 70B Instruct does not fit (smallest quant needs 26.1 GB); Qwen3.8 27B fits at Q3_K_L.
Which has a longer context window, Llama 3.1 70B Instruct or Qwen3.8 27B?
Llama 3.1 70B Instruct supports 131,072 tokens and Qwen3.8 27B supports 262,144 tokens.