Можно ли учить английский сленг по Reddit в SubRead?

Да. SubRead — спокойный ридер для изучающих язык: открой тред, выдели фразу, получи разбор сленга и тона и сохрани карточку.

Что такое Language Lab?

Короткие паки английского (время, смягчения, интернет-сленг, реакции) под реальную речь Reddit — потом спроси Iggy примеры под свой уровень.

Подойдёт ли SubRead для ESL / среднего уровня?

Да, это иммерсия: живые посты и комментарии вместо учебниковых диалогов, с AI-помощью, когда тон или сленг непонятны.

Нужен ли аккаунт?

Читать можно без входа. Войди, чтобы сохранять карточки, избранное, чат с Iggy и профиль обучения.

r/LocalLLaMA · u/consultkitapp1 авг. 2026 г., 16:53

DeepSeek-V4-Flash-0731-UD-Q3_K_XL 3x3090 test results

For anyone interested, here are the llama-bench results on 3 bit K_XL quantization. I think this could be pushed further but no luck so far.

Command

./llama-bench -m /home/user/llamacpp/modelsmain/unsloth/ds4/DeepSeek-V4-Flash-0731-UD-Q3_K_XL-00001-of-00004.gguf -ngl 21 --split-mode layer -p 512 -n 128 -r 5

Output

CUDA Initialization

ggml_cuda_init: found 3 CUDA devices (Total VRAM: 72364 MiB): Device 0: NVIDIA GeForce RTX 3090, compute capability 8.6, VMM: yes, VRAM: 24116 MiB Device 1: NVIDIA GeForce RTX 3090, compute capability 8.6, VMM: yes, VRAM: 24124 MiB Device 2: NVIDIA GeForce RTX 3090, compute capability 8.6, VMM: yes, VRAM: 24124 MiB

Benchmark Results

Model	Size	Params	Backend	NGL	Test	T/S
deepseek4 ?B Q3_K - Medium	119.40 GiB	284.33 B	CUDA	21	pp512	116.04 ± 27.64
deepseek4 ?B Q3_K - Medium	119.40 GiB	284.33 B	CUDA	21	tg128	7.71 ± 0.09

Build: e3546c794 (9976)

System Memory

Total	Used	Free	Shared	Buff/Cache	Available
Mem	122Gi	7.9Gi	1.2Gi	165Mi	114Gi
Swap	0B	0B	0B

NVIDIA-SMI Status

+-----------------------------------------------------------------------------------------+

| NVIDIA-SMI 580.159.03 Driver Version: 580.159.03 CUDA Version: 13.0 |

+-----------------------------------------+------------------------+----------------------+

| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |

| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |

| | | MIG M. |

|=========================================+========================+======================|

| 0 NVIDIA GeForce RTX 3090 Off | 00000000:01:00.0 Off | N/A |

| 58% 55C P2 151W / 390W | 22696MiB / 24576MiB | 35% Default |

| | | N/A |

+-----------------------------------------+------------------------+----------------------+

| 1 NVIDIA GeForce RTX 3090 Off | 00000000:31:00.0 On | N/A |

| 30% 53C P2 120W / 350W | 20193MiB / 24576MiB | 9% Default |

| | | N/A |

+-----------------------------------------+------------------------+----------------------+

| 2 NVIDIA GeForce RTX 3090 Off | 00000000:6C:00.0 Off | N/A |

| 32% 55C P2 135W / 420W | 17811MiB / 24576MiB | 0% Default |

| | | N/A |

+-----------------------------------------+------------------------+----------------------+

u/wombweed11 авг. 2026 г., 16:57
PP t/s vs TG t/s?
- u/consultkitapp11 авг. 2026 г., 16:58
  output:
  
  ggml_cuda_init: found 3 CUDA devices (Total VRAM: 72364 MiB):
  
  Device 0: NVIDIA GeForce RTX 3090, compute capability 8.6, VMM: yes, VRAM: 24116 MiB
  
  Device 1: NVIDIA GeForce RTX 3090, compute capability 8.6, VMM: yes, VRAM: 24124 MiB
  
  Device 2: NVIDIA GeForce RTX 3090, compute capability 8.6, VMM: yes, VRAM: 24124 MiB
  
  | model | size | params | backend | ngl | test | t/s |
  
  | ------------------------------ | ---------: | ---------: | ---------- | --: | --------------: | -------------------: |
  
  | deepseek4 ?B Q3_K - Medium | 119.40 GiB | 284.33 B | CUDA | 21 | ---->pp512 | 116.04 ± 27.64 |
  
  | deepseek4 ?B Q3_K - Medium | 119.40 GiB | 284.33 B | CUDA | 21 | ---->tg128 | 7.71 ± 0.09 |
  - u/consultkitapp11 авг. 2026 г., 16:59
  - u/wombweed11 авг. 2026 г., 17:002 ответа
u/jacek202311 авг. 2026 г., 17:11
Thanks I will download Q3 at some point for my 4x3090
u/TokenRingAI11 авг. 2026 г., 17:13
For comparison, on Strix Halo I am seeing 15 tk/s TG, and 200tk/s PP at short length using ROCm and a similar 3 bit quant, no speculative decoding, and full quality KV cache
- u/crantob11 авг. 2026 г., 17:36
  Nice win for your device. Tempting... Tempting...
  - u/TokenRingAI11 авг. 2026 г., 18:02
u/[deleted]11 авг. 2026 г., 17:37
[removed]
u/Treidge11 авг. 2026 г., 18:19
You should really try to bump up batch sizes. In my case, going from default (I believe its 512) to 4096 increased preprocessing from 75 to 270 t/s. With 8192 it became 450 t/s (single RTX 5090). So, do try -b 4096 -ub 4096, and then -b 8192 -ub 8192. Should make a big difference for PP.
u/Klutzy-Snow801611 авг. 2026 г., 18:41
Native precision is only 1/3 larger since the model was released in 4-bit. I get 10 tokens per second generation on a similar system, so it's an option if you care about accuracy.
u/thefooz11 авг. 2026 г., 19:45
I’m getting 31 tok/s TG and \~200 tok/s PP on an M4 Max MacBook using Antirez DS4. That’s running a 2-4bit imatrix mixed precision version. I recommend giving it a shot over llama.cpp, which still isn’t properly optimized for the deepseek architecture.

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