u/Dzhmelyk135

I don't know what's happening.

So, when i use models in openwebui everything works, ollama uses the GPU fully, everything's perfect.

Then, I use Hermes, and loads the model in VRAM (fully, no offload needed), but then it starts using 50% of the CPU and the GPU spikes for 1 second every 8, and obviously it's slow. Nothing is in RAM.

Same thing in Qwen Code, I can't figure out why.

Before it worked. The same models work in openwebui.

Specs:

Intel xeon e5 2680 V4

32 GB DDR4 2133 MHz

Nvidia Tesla P100 16 GB

OS: Truenas scale (ollama in docker, official app)

Thanks in advance

Logs from when it did it with Qwen Code:

• load_tensors: loading model tensors, this can take a while... (load_mode = mmap)
• load_tensors: offloading output layer to GPU
• load_tensors: offloading 47 repeating layers to GPU
• load_tensors: offloaded 49/49 layers to GPU
• load_tensors: CPU_Mapped model buffer size = 787.50 MiB
• load_tensors: CUDA0 model buffer size = 7024.41 MiB
  • time=2026-08-16T17:37:17.185Z
  • level=INFO
  • source=llama_server.go:1348
  • msg=waiting for llama-server to become available
  • status=llm server not responding
  • time=2026-08-16T17:37:17.480Z
  • level=INFO
  • source=llama_server.go:1348
  • msg=waiting for llama-server to become available
  • status=llm server loading model
• cmn common_init_: added <eos> logit bias = -inf
• cmn common_init_: added <|tool_response> logit bias = -inf
• cmn common_init_: added <turn|> logit bias = -inf
• llama_init_from_model: enabling flash_attn since it is required for quantized V cache
• llama_context: constructing llama_context
• llama_context: n_seq_max = 1
• llama_context: n_ctx = 65536
• llama_context: n_ctx_seq = 65536
• llama_context: n_batch = 1024
• llama_context: n_ubatch = 1024
• llama_context: causal_attn = 1
• llama_context: flash_attn = enabled
• llama_context: kv_unified = false
• llama_context: freq_base = 1000000.0
• llama_context: freq_scale = 1
• llama_context: n_rs_seq = 0
• llama_context: n_outputs_max = 1
• llama_context: n_outputs_max_per_seq = 1
• llama_context: n_ctx_seq (65536) < n_ctx_train (262144) -- the full capacity of the model will not be utilized
• llama_context: CUDA_Host output buffer size = 1.00 MiB
• llama_kv_cache_iswa: creating non-SWA KV cache, size = 65536 cells
• llama_kv_cache: CUDA0 KV buffer size = 352.00 MiB
• llama_kv_cache: size = 352.00 MiB ( 65536 cells, 8 layers, 1/1 seqs), K (q5_0): 176.00 MiB, V (q5_0): 176.00 MiB
• llama_kv_cache: attn_rot_k = 1, n_embd_head_k_all = 512
• llama_kv_cache: attn_rot_v = 1, n_embd_head_k_all = 512
• llama_kv_cache_iswa: creating SWA KV cache, size = 2048 cells
• llama_kv_cache: CUDA0 KV buffer size = 220.00 MiB
• llama_kv_cache: size = 220.00 MiB ( 2048 cells, 40 layers, 1/1 seqs), K (q5_0): 110.00 MiB, V (q5_0): 110.00 MiB
• llama_kv_cache: attn_rot_k = 1, n_embd_head_k_all = 256
• llama_kv_cache: attn_rot_v = 1, n_embd_head_k_all = 256
• sched_reserve: reserving ...
• resolve_fused_ops: resolving fused Gated Delta Net support:
• resolve_fused_ops: fused Gated Delta Net (autoregressive) enabled
• resolve_fused_ops: fused Gated Delta Net (chunked) enabled
• resolve_fused_ops: resolving fused Lightning Indexer support:
• resolve_fused_ops: Lightning Indexer enabled
• resolve_fused_ops: resolving fused DeepSeek V4 HC support:
• resolve_fused_ops: fused DeepSeek V4 HC pre enabled
• resolve_fused_ops: fused DeepSeek V4 HC comb enabled
• resolve_fused_ops: fused DeepSeek V4 HC post enabled
• sched_reserve: CUDA0 compute buffer size = 212.32 MiB
• sched_reserve: CUDA_Host compute buffer size = 256.32 MiB
• sched_reserve: graph nodes = 2545
• sched_reserve: graph splits = 98
• sched_reserve: reserve took 218.65 ms, sched copies = 1
• cmn common_init_: KV cache shifting is not supported for this context, disabling KV cache shifting
• cmn common_init_: warming up the model with an empty run - please wait ... (--no-warmup to disable)
• clip_model_loader: model name:
• clip_model_loader: description:
• clip_model_loader: GGUF version: 3
• clip_model_loader: alignment: 32
• clip_model_loader: n_tensors: 11
• clip_model_loader: n_kv: 36
• clip_model_loader: has vision encoder
• clip_model_loader: has audio encoder
• clip_ctx: CLIP using CUDA0 backend
• load_hparams: projector: gemma4uv
• load_hparams: n_embd: 3840
• load_hparams: n_head: 0
• load_hparams: n_ff: 0
• load_hparams: n_layer: 0
• load_hparams: ffn_op: gelu_quick
• load_hparams: projection_dim: 3840
• --- vision hparams ---
• load_hparams: image_size: 224
• load_hparams: patch_size: 48
• load_hparams: has_llava_proj: 0
• load_hparams: minicpmv_version: 0
• load_hparams: n_merge: 1
• load_hparams: n_wa_pattern: 0
• load_hparams: image_min_pixels: 92160
• load_hparams: image_max_pixels: 645120
• load_hparams: model size: 167.00 MiB
• load_hparams: metadata size: 0.00 MiB
• get_dummy_batch: warmup with image size = 768 x 768
• get_dummy_batch: warmup with image size = 768 x 768
• reserve_compute_meta: CUDA0 compute buffer size = 13.53 MiB
• reserve_compute_meta: CPU compute buffer size = 6.75 MiB
• reserve_compute_meta: graph splits = 1, nodes = 21
• warmup: flash attention is enabled
• clip_ctx: CLIP using CUDA0 backend
• load_hparams: projector: gemma4ua
• load_hparams: n_embd: 640
• load_hparams: n_head: 0
• load_hparams: n_ff: 0
• load_hparams: n_layer: 0
• load_hparams: ffn_op: gelu_quick
• load_hparams: projection_dim: 3840
• --- audio hparams ---
• load_hparams: n_mel_bins: 640
• load_hparams: proj_stack_factor: 0
• load_hparams: audio_chunk_len: 0
• load_hparams: audio_sample_rate: 16000
• load_hparams: audio_n_fft: -1
• load_hparams: audio_window_len: -1
• load_hparams: audio_hop_len: -1
• load_hparams: model size: 167.00 MiB
• load_hparams: metadata size: 0.00 MiB
• get_dummy_batch: warmup with audio size = 3000
• get_dummy_batch: warmup with audio size = 3000
• reserve_compute_meta: CUDA0 compute buffer size = 58.59 MiB
• reserve_compute_meta: CPU compute buffer size = 7.32 MiB
• reserve_compute_meta: graph splits = 1, nodes = 4
• warmup: flash attention is enabled
• init_audio: audio input is in experimental stage and may have reduced quality:
• https://github.com/ggml-org/llama.cpp/discussions/13759
• srv load_model: loaded multimodal model, '/root/.ollama/models/blobs/sha256-675ad6e68101ca9413ec806855c452362f0213f2dfc5800996b086fdb8119842'
• srv load_model: initializing, n_slots = 1, n_ctx_slot = 65536, kv_unified = 'false'
• spec common_specu: no implementations specified for speculative decoding
• slot load_model: id 0 | task -1 | new slot, n_ctx = 65536
• srv load_model: prompt cache is disabled - use `--cache-ram N` to enable it
• srv load_model: for more info see https://github.com/ggml-org/llama.cpp/pull/16391
• srv load_model: context checkpoints enabled, max = 32, min spacing = 8192
• srv init: --cache-idle-slots requires --cache-ram, disabling
• srv init: init: chat template, example_format: '<|im_start|>system
• You are a helpful assistant<|im_end|>
• <|im_start|>user
• Hello<|im_end|>
• <|im_start|>assistant
• Hi there<|im_end|>
• <|im_start|>user
• How are you?<|im_end|>
• <|im_start|>assistant
• '
• srv init: init: chat template, thinking = 0
• srv llama_server: model loaded
• srv llama_server: listening on http://127.0.0.1:45959
• srv update_slots: all slots are idle
  • time=2026-08-16T17:37:25.540Z
  • level=INFO
  • source=llama_server.go:1360
  • msg=llama-server started in 23.82 seconds
  • time=2026-08-16T17:37:25.762Z
  • level=INFO
  • source=images.go:374
  • msg=template selection
  • model=registry.ollama.ai/library/gemma4-hermes:latest
  • selected=renderer_parser
  • renderer=gemma4
  • parser=gemma4
  • go_template=null
  • chat_template=[tools completion vision audio thinking]
  • harmony=null
  • renderer_parser=[completion vision audio tools thinking]
  • time=2026-08-16T17:37:25.763Z
  • level=INFO
  • source=sched.go:728
  • msg=loaded runners
  • count=1
  • time=2026-08-16T17:37:25.763Z
  • level=INFO
  • source=llama_server.go:1293
  • msg=waiting for llama-server to start responding
  • time=2026-08-16T17:37:25.764Z
  • level=INFO
  • source=llama_server.go:1360
  • msg=llama-server started in 24.06 seconds
• srv server_strea: conv_id= (empty=1)
• slot get_availabl: id 0 | task -1 | - skipping, slot is empty
• slot get_availabl: id 0 | task -1 | selected slot by LRU, t_last = -1
• slot launch_slot_: id 0 | task -1 | sampler chain: logits -> logit-bias -> ?penalties -> ?dry -> ?top-n-sigma -> top-k -> ?typical -> ?top-p -> ?min-p -> ?xtc -> ?temp-ext -> dist
• slot launch_slot_: id 0 | task -1 | sampler params:
• repeat_last_n = 64, repeat_penalty = 1.000, frequency_penalty = 0.000, presence_penalty = 0.000
• dry_multiplier = 0.000, dry_base = 1.750, dry_allowed_length = 2, dry_penalty_last_n = 64
• top_k = 64, top_p = 1.000, min_p = 0.000, xtc_probability = 0.000, xtc_threshold = 0.100, typical_p = 1.000, top_n_sigma = -1.000, temp = 1.000
• mirostat = 0, mirostat_lr = 0.100, mirostat_ent = 5.000, adaptive_target = -1.000, adaptive_decay = 0.900
• slot launch_slot_: id 0 | task 0 | processing task, is_child = 0
• slot operator(): id 0 | task 0 | new prompt, n_ctx_slot = 65536, n_keep = 4, task.n_tokens = 34116
• slot operator(): id 0 | task 0 | cached n_tokens = 0, memory_seq_rm [0, end)
• slot print_timing: id 0 | task 0 | prompt processing, n_tokens = 1024, progress = 0.03, t = 27.04 s / 37.87 tokens per second
• slot operator(): id 0 | task 0 | cached n_tokens = 1024, memory_seq_rm [1024, end)
• slot print_timing: id 0 | task 0 | prompt processing, n_tokens = 2048, progress = 0.06, t = 77.52 s / 26.42 tokens per second
• slot operator(): id 0 | task 0 | cached n_tokens = 2048, memory_seq_rm [2048, end)
• slot print_timing: id 0 | task 0 | prompt processing, n_tokens = 3072, progress = 0.09, t = 139.16 s / 22.08 tokens per second
• slot operator(): id 0 | task 0 | cached n_tokens = 3072, memory_seq_rm [3072, end)
• slot print_timing: id 0 | task 0 | prompt processing, n_tokens = 4096, progress = 0.12, t = 211.24 s / 19.39 tokens per second
• blablabla same thing again again again.

[removed]

A nice read, go check it out

(Sorry if repost :D)

Link in the first comment

Started overclocking this computer to run retro games better.

I found the limit of the CPU pretty quickly:

By increasing the FBS I could go to up 225 MHz (+25 on stock, X10 multiplier on the core clock), even if the real limit was the PATA controller, since I started getting CRC errors.

On the GPU however... I still haven't found the limit.

I am getting crazy scores on 3dmark 2001, on stock i would get mabye 7000 points, now I'm getting 9300 easily. The core is running at 470 MHz (350 stock) and the VRAM at 715 MHz (550 MHz stock I think). I could get more from the CPU, but I'm limited by the PATA controller as I said before, but increasing the GPU is still helping a lot.

It's also an OEM pc, with a really locked down BIOS, so software overclock is helping a lot.

EDIT: Finally found the GPU limit: It started artifacring at an incredible 510 MHz on the core and 780 MHz on the memory. Max ever reached: 9500 points. Not going to use it like this, I'm scaling back a bit the GPU and vram just for stability, even if it didn't get me any artifacting.

Tried to connect 3 MQ sensors on a breadboard...

Lesson learned

My analog multimeter was set on current and it went to the max possible reading