Llama 3.1 8B Instruct vs Qwen3.8 27B

Qwen3.8 27B leads llmrun Score 121.5 to 62.5; both fit 24 GB at Q4_K_M; Llama 3.1 8B Instruct is 3.3× faster on RTX 4090 (~124 vs ~38 tok/s).

Model A · Meta

Llama 3.1 8B Instruct

62.5llmrun Score

8.0B · 128K context · Llama 3.1 Community · Jul 2024

ChatFunctions

Model B · Alibaba

Qwen3.8 27B

121.5llmrun Score

27.8B · 256K context · Apache 2.0 · Aug 2026

VisionFunctions

Which should you choose?

Choose Llama 3.1 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 62.5
Stronger at science
112.0 vs 55.5
Stronger at coding
112.1 vs 55.9
Ahead on the benchmarks
5 of 5 shared boards
Longer context window
256K vs 128K tokens
Also does vision
Llama 3.1 8B Instruct doesn't list it

On your hardware

Each model at its default quant (Q4_K_M), a smaller one when it must. Estimates, ±20%.

MemoryLlama 3.1 8B InstructQwen3.8 27B

8 GB

RTX 4060 · 8 GB tier

Llama 3.1 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

Llama 3.1 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

Llama 3.1 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

Llama 3.1 8B Instruct

Q4_K_M · ~124 tok/s

7.9 GB · up to 64Kfaster

Qwen3.8 27B

Q4_K_M · ~38 tok/s

17.9 GB · up to 64K

32 GB

RTX 5090 · 32 GB tier

Llama 3.1 8B Instruct

Q4_K_M · ~220 tok/s

7.9 GB · up to 128Kfaster

Qwen3.8 27B

Q4_K_M · ~67 tok/s

17.9 GB · up to 256K

48 GB

RTX A6000 · 48 GB tier

Llama 3.1 8B Instruct

Q4_K_M · ~94 tok/s

7.9 GB · up to 128Kfaster

Qwen3.8 27B

Q4_K_M · ~29 tok/s

17.9 GB · up to 256K

Mac 32 GB

Mac Mini M4 · Macs

Llama 3.1 8B Instruct

Q4_K_M · ~16 tok/s

7.9 GB · up to 128Kfaster

Qwen3.8 27B

Q4_K_M · ~5 tok/s

17.9 GB · up to 128K

Mac 64 GB

Mac Studio M4 Max · Macs

Llama 3.1 8B Instruct

Q4_K_M · ~72 tok/s

7.9 GB · up to 128Kfaster

Qwen3.8 27B

Q4_K_M · ~22 tok/s

17.9 GB · up to 256K

Mac 128 GB

Mac Studio M4 Max · Macs

Llama 3.1 8B Instruct

Q4_K_M · ~72 tok/s

7.9 GB · up to 128Kfaster

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).

llmrun Score

with its 90% range · bars share one scale

Llama 3.1 8B Instruct Qwen3.8 27B

Overall

Llama 3.1 8B Instruct

62.556–70

Qwen3.8 27B

121.5119–124

Coding

Llama 3.1 8B Instruct

55.933–79

Qwen3.8 27B

112.1105–119

Math

Llama 3.1 8B Instruct

no data

Qwen3.8 27B

117.5111–125

Science

Llama 3.1 8B Instruct

55.536–76

Qwen3.8 27B

112.093–131

Reasoning

Llama 3.1 8B Instruct

69.356–82

Qwen3.8 27B

124.1121–127

Llama 3.1 8B Instruct · #88 of 92 open models · #31 of 34 that fit 24 GB

Qwen3.8 27B · #10 of 92 open models · #1 of 34 that fit 24 GB

Benchmarks

5 shared boards: Llama 3.1 8B Instruct leads 0, Qwen3.8 27B leads 5 · Benchmarks imported Oct 2026

BoardLlama 3.1 8B InstructQwen3.8 27B

LMArena Text

Llama 3.1 8B Instruct

1211.3#141/186 open

ArenaLMArena · Oct 2026

Qwen3.8 27B

1438.1#22/186 open

ArenaLMArena · Oct 2026

SciCode

Llama 3.1 8B Instruct

13.2%#64/65 open

Lab-reportedEpoch AI · Jul 2024

Qwen3.8 27B

46.6%#19/65 open

Lab-reportedEpoch AI · Aug 2026

CritPt

Llama 3.1 8B Instruct

0.0%#52/65 open

Lab-reportedEpoch AI · Jul 2024

Qwen3.8 27B

5.4%#15/65 open

Lab-reportedEpoch AI · Aug 2026

DTBench

Llama 3.1 8B Instruct

50.9%#59/68 open

Lab-reportedEpoch AI · Jul 2024

Qwen3.8 27B

88.0%#9/68 open

Lab-reportedEpoch AI · Aug 2026

LMCA

Llama 3.1 8B Instruct

5.4%#51/54 open

Lab-reportedEpoch AI · Jul 2024

Qwen3.8 27B

41.4%#7/54 open

Lab-reportedEpoch AI · Aug 2026

20 more boards (other shared and single-model boards)

Only Llama 3.1 8B Instruct (11)

GPQA Diamond27.0%
AIME 2024/20251.7%
MMLU-Pro44.3%
MATH Level 522.9%
MMLU56.1%
WeirdML1.7%
Chess Puzzles0.0%
BALROG15.1%
GSM8K82.4%
PIQA81.2%
BoolQ82.8%

Only Qwen3.8 27B (9)

VRAM by quantization

Memory to load the model and 2K context; below it, the total at 32K and 128K context. Lighter side in bold.

QuantLlama 3.1 8B InstructQwen3.8 27B

Q2_K

Llama 3.1 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

Llama 3.1 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

Llama 3.1 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

Llama 3.1 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

Llama 3.1 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

Llama 3.1 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

Llama 3.1 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

Llama 3.1 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

Llama 3.1 8B Instruct

2.4 GB2.5 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

Llama 3.1 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

Llama 3.1 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

Llama 3.1 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

Llama 3.1 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

Llama 3.1 8B Instruct

3.5 GB3.0 GB file

32K 9.0 GB · 128K 26.7 GB

Qwen3.8 27B

not listed

IQ3_XS

Llama 3.1 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

Llama 3.1 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

Llama 3.1 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

Llama 3.1 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

Llama 3.1 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

Llama 3.1 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

Llama 3.1 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

Llama 3.1 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

Llama 3.1 8B Instruct

5.4 GB5.3 GB file

32K 10.9 GB · 128K 28.5 GB

Qwen3.8 27B

17.8 GB18.8 GB file

32K 19.1 GB · 128K 24.5 GB

Q5_0

Llama 3.1 8B Instruct

5.5 GB5.6 GB file

32K 11.0 GB · 128K 28.6 GB

Qwen3.8 27B

not listed

Q5_1

Llama 3.1 8B Instruct

6.1 GB6.1 GB file

32K 11.6 GB · 128K 29.2 GB

Qwen3.8 27B

not listed

Q5_K_S

Llama 3.1 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

Llama 3.1 8B Instruct

6.4 GB6.1 GB file

32K 11.9 GB · 128K 29.5 GB

Qwen3.8 27B

20.9 GB21.5 GB file

32K 22.2 GB · 128K 27.6 GB

BF16

Llama 3.1 8B Instruct

17.7 GB16.1 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

How to run each

Llama 3.1 8B Instruct

Ollama · Q4_K_M

$ollama run hf.co/bartowski/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_K_M

All ways to run Llama 3.1 8B Instruct →

Qwen3.8 27B

Ollama · Q4_K_M

$ollama run hf.co/unsloth/Qwen3.8-27B-GGUF:Q4_K_M

All ways to run Qwen3.8 27B →

Specifications

Specifications of Llama 3.1 8B Instruct and Qwen3.8 27B
Spec	Llama 3.1 8B Instruct	Qwen3.8 27B
Publisher	Meta	Alibaba
Parameters	8.0B	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 8B Instruct or Qwen3.8 27B?

Qwen3.8 27B leads llmrun Score 121.5 to 62.5; both fit 24 GB at Q4_K_M; Llama 3.1 8B Instruct is 3.3× faster on RTX 4090 (~124 vs ~38 tok/s). Choose Llama 3.1 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 science.

Which needs less VRAM, Llama 3.1 8B Instruct or Qwen3.8 27B?

At Q4_K_M, Llama 3.1 8B Instruct requires 5.3 GB while Qwen3.8 27B needs 17.4 GB, making Llama 3.1 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 Llama 3.1 8B Instruct (Q4_K_M) and 38 tok/s for Qwen3.8 27B (Q4_K_M), so Llama 3.1 8B Instruct is faster. Estimates, ±20%.

Can I run Llama 3.1 8B Instruct or Qwen3.8 27B on a 16 GB GPU?

Llama 3.1 8B Instruct fits at Q4_K_M; Qwen3.8 27B fits at Q3_K_L.

Which has a longer context window, Llama 3.1 8B Instruct or Qwen3.8 27B?

Llama 3.1 8B Instruct supports 131,072 tokens and Qwen3.8 27B supports 262,144 tokens.