ggml_cuda_init: found 1 CUDA devices (Total VRAM: 124610 MiB):
  Device 0: NVIDIA GB10, compute capability 12.1, VMM: yes, VRAM: 124610 MiB
system info: n_threads = 20, n_threads_batch = 20, total_threads = 20

system_info: n_threads = 20 (n_threads_batch = 20) / 20 | CUDA : ARCHS = 1210 | USE_GRAPHS = 1 | PEER_MAX_BATCH_SIZE = 128 | FA_ALL_QUANTS = 1 | BLACKWELL_NATIVE_FP4 = 1 | CPU : NEON = 1 | ARM_FMA = 1 | LLAMAFILE = 1 | OPENMP = 1 | REPACK = 1 | 

Running without SSL
init: using 19 threads for HTTP server
start: binding port with default address family
main: loading model
srv    load_model: loading model '/home/coder/models/gemma-4-26B-A4B-it-f16.gguf'
llama_model_load_from_file_impl: using device CUDA0 (NVIDIA GB10) (000f:01:00.0) - 116297 MiB free
llama_model_loader: loaded meta data with 47 key-value pairs and 658 tensors from /home/coder/models/gemma-4-26B-A4B-it-f16.gguf (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv   0:                       general.architecture str              = gemma4
llama_model_loader: - kv   1:                               general.type str              = model
llama_model_loader: - kv   2:                     general.sampling.top_k i32              = 64
llama_model_loader: - kv   3:                     general.sampling.top_p f32              = 0.950000
llama_model_loader: - kv   4:                      general.sampling.temp f32              = 1.000000
llama_model_loader: - kv   5:                         general.size_label str              = 128x2.6B
llama_model_loader: - kv   6:                            general.license str              = apache-2.0
llama_model_loader: - kv   7:                       general.license.link str              = https://ai.google.dev/gemma/docs/gemm...
llama_model_loader: - kv   8:                               general.tags arr[str,1]       = ["image-text-to-text"]
llama_model_loader: - kv   9:                         gemma4.block_count u32              = 30
llama_model_loader: - kv  10:                      gemma4.context_length u32              = 262144
llama_model_loader: - kv  11:                    gemma4.embedding_length u32              = 2816
llama_model_loader: - kv  12:                 gemma4.feed_forward_length u32              = 2112
llama_model_loader: - kv  13:                gemma4.attention.head_count u32              = 16
llama_model_loader: - kv  14:             gemma4.attention.head_count_kv arr[i32,30]      = [8, 8, 8, 8, 8, 2, 8, 8, 8, 8, 8, 2, ...
llama_model_loader: - kv  15:                      gemma4.rope.freq_base f32              = 1000000.000000
llama_model_loader: - kv  16:                  gemma4.rope.freq_base_swa f32              = 10000.000000
llama_model_loader: - kv  17:    gemma4.attention.layer_norm_rms_epsilon f32              = 0.000001
llama_model_loader: - kv  18:                        gemma4.expert_count u32              = 128
llama_model_loader: - kv  19:                   gemma4.expert_used_count u32              = 8
llama_model_loader: - kv  20:                gemma4.attention.key_length u32              = 512
llama_model_loader: - kv  21:              gemma4.attention.value_length u32              = 512
llama_model_loader: - kv  22:                          general.file_type u32              = 1
llama_model_loader: - kv  23:             gemma4.final_logit_softcapping f32              = 30.000000
llama_model_loader: - kv  24:            gemma4.attention.sliding_window u32              = 1024
llama_model_loader: - kv  25:          gemma4.attention.shared_kv_layers u32              = 0
llama_model_loader: - kv  26:    gemma4.embedding_length_per_layer_input u32              = 0
llama_model_loader: - kv  27:    gemma4.attention.sliding_window_pattern arr[bool,30]     = [true, true, true, true, true, false,...
llama_model_loader: - kv  28:            gemma4.attention.key_length_swa u32              = 256
llama_model_loader: - kv  29:          gemma4.attention.value_length_swa u32              = 256
llama_model_loader: - kv  30:          gemma4.expert_feed_forward_length u32              = 704
llama_model_loader: - kv  31:                gemma4.rope.dimension_count u32              = 512
llama_model_loader: - kv  32:            gemma4.rope.dimension_count_swa u32              = 256
llama_model_loader: - kv  33:               general.quantization_version u32              = 2
llama_model_loader: - kv  34:                       tokenizer.ggml.model str              = gemma4
llama_model_loader: - kv  35:                      tokenizer.ggml.tokens arr[str,262144]  = ["<pad>", "<eos>", "<bos>", "<unk>", ...
llama_model_loader: - kv  36:                      tokenizer.ggml.scores arr[f32,262144]  = [-1000.000000, -1000.000000, -1000.00...
llama_model_loader: - kv  37:                  tokenizer.ggml.token_type arr[i32,262144]  = [3, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv  38:                      tokenizer.ggml.merges arr[str,514906]  = ["\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n", ...
llama_model_loader: - kv  39:                tokenizer.ggml.bos_token_id u32              = 2
llama_model_loader: - kv  40:                tokenizer.ggml.eos_token_id u32              = 1
llama_model_loader: - kv  41:            tokenizer.ggml.unknown_token_id u32              = 3
llama_model_loader: - kv  42:            tokenizer.ggml.padding_token_id u32              = 0
llama_model_loader: - kv  43:               tokenizer.ggml.mask_token_id u32              = 4
llama_model_loader: - kv  44:                    tokenizer.chat_template str              = {%- macro format_parameters(propertie...
llama_model_loader: - kv  45:            tokenizer.ggml.add_space_prefix bool             = false
llama_model_loader: - kv  46:               tokenizer.ggml.add_bos_token bool             = false
llama_model_loader: - type  f32:  392 tensors
llama_model_loader: - type  f16:  266 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type   = F16
print_info: file size   = 47.02 GiB (16.01 BPW) 
load: 0 unused tokens
load: control-looking token:    212 '</s>' was not control-type; this is probably a bug in the model. its type will be overridden
load: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
load: printing all EOG tokens:
load:   - 1 ('<eos>')
load:   - 106 ('<turn|>')
load:   - 212 ('</s>')
load: special tokens cache size = 25
load: token to piece cache size = 1.9445 MB
print_info: arch                  = gemma4
print_info: vocab_only            = 0
print_info: no_alloc              = 0
print_info: n_ctx_train           = 262144
print_info: n_embd                = 2816
print_info: n_embd_inp            = 2816
print_info: n_layer               = 30
print_info: n_head                = 16
print_info: n_head_kv             = [8, 8, 8, 8, 8, 2, 8, 8, 8, 8, 8, 2, 8, 8, 8, 8, 8, 2, 8, 8, 8, 8, 8, 2, 8, 8, 8, 8, 8, 2]
print_info: n_rot                 = 512
print_info: n_swa                 = 1024
print_info: is_swa_any            = 1
print_info: n_embd_head_k         = 512
print_info: n_embd_head_v         = 512
print_info: n_gqa                 = [2, 2, 2, 2, 2, 8, 2, 2, 2, 2, 2, 8, 2, 2, 2, 2, 2, 8, 2, 2, 2, 2, 2, 8, 2, 2, 2, 2, 2, 8]
print_info: n_embd_k_gqa          = [2048, 2048, 2048, 2048, 2048, 1024, 2048, 2048, 2048, 2048, 2048, 1024, 2048, 2048, 2048, 2048, 2048, 1024, 2048, 2048, 2048, 2048, 2048, 1024, 2048, 2048, 2048, 2048, 2048, 1024]
print_info: n_embd_v_gqa          = [2048, 2048, 2048, 2048, 2048, 1024, 2048, 2048, 2048, 2048, 2048, 1024, 2048, 2048, 2048, 2048, 2048, 1024, 2048, 2048, 2048, 2048, 2048, 1024, 2048, 2048, 2048, 2048, 2048, 1024]
print_info: f_norm_eps            = 0.0e+00
print_info: f_norm_rms_eps        = 1.0e-06
print_info: f_clamp_kqv           = 0.0e+00
print_info: f_max_alibi_bias      = 0.0e+00
print_info: f_logit_scale         = 0.0e+00
print_info: f_attn_scale          = 1.0e+00
print_info: n_ff                  = 2112
print_info: n_expert              = 128
print_info: n_expert_used         = 8
print_info: n_expert_groups       = 0
print_info: n_group_used          = 0
print_info: causal attn           = 1
print_info: pooling type          = -1
print_info: rope type             = 2
print_info: rope scaling          = linear
print_info: freq_base_train       = 1000000.0
print_info: freq_scale_train      = 1
print_info: freq_base_swa         = 10000.0
print_info: freq_scale_swa        = 1
print_info: n_embd_head_k_swa     = 256
print_info: n_embd_head_v_swa     = 256
print_info: n_rot_swa             = 256
print_info: n_ctx_orig_yarn       = 262144
print_info: rope_yarn_log_mul     = 0.0000
print_info: rope_finetuned        = unknown
print_info: model type            = ?B
print_info: model params          = 25.23 B
print_info: general.name          = n/a
print_info: vocab type            = SPM
print_info: n_vocab               = 262144
print_info: n_merges              = 0
print_info: BOS token             = 2 '<bos>'
print_info: EOS token             = 1 '<eos>'
print_info: UNK token             = 3 '<unk>'
print_info: PAD token             = 0 '<pad>'
print_info: MASK token            = 4 '<mask>'
print_info: LF token              = 248 '<0x0A>'
print_info: EOG token             = 1 '<eos>'
print_info: EOG token             = 106 '<turn|>'
print_info: EOG token             = 212 '</s>'
print_info: max token length      = 93
load_tensors: loading model tensors, this can take a while... (mmap = false, direct_io = false)
str: cannot properly format tensor name output with suffix=weight bid=-1 xid=-1
load_tensors: offloading output layer to GPU
load_tensors: offloading 29 repeating layers to GPU
load_tensors: offloaded 31/31 layers to GPU
load_tensors:        CUDA0 model buffer size = 48150.36 MiB
load_tensors:    CUDA_Host model buffer size =  1408.00 MiB
........................................................................
common_init_result: added <eos> logit bias = -inf
common_init_result: added <turn|> logit bias = -inf
common_init_result: added </s> logit bias = -inf
llama_context: constructing llama_context
llama_context: n_seq_max     = 1
llama_context: n_ctx         = 262144
llama_context: n_ctx_seq     = 262144
llama_context: n_batch       = 2048
llama_context: n_ubatch      = 2048
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:  CUDA_Host  output buffer size =     1.00 MiB
llama_kv_cache_iswa: creating non-SWA KV cache, size = 262144 cells
llama_kv_cache:      CUDA0 KV buffer size =  5120.00 MiB
llama_kv_cache: size = 5120.00 MiB (262144 cells,   5 layers,  1/1 seqs), K (f16): 2560.00 MiB, V (f16): 2560.00 MiB
llama_kv_cache: attn_rot_k = 0
llama_kv_cache: attn_rot_v = 0
llama_kv_cache_iswa: creating     SWA KV cache, size = 3072 cells
llama_kv_cache:      CUDA0 KV buffer size =   600.00 MiB
llama_kv_cache: size =  600.00 MiB (  3072 cells,  25 layers,  1/1 seqs), K (f16):  300.00 MiB, V (f16):  300.00 MiB
llama_kv_cache: attn_rot_k = 0
llama_kv_cache: attn_rot_v = 0
sched_reserve: reserving ...
sched_reserve: resolving fused Gated Delta Net support:
sched_reserve: fused Gated Delta Net (autoregressive) enabled
sched_reserve: fused Gated Delta Net (chunked) enabled
sched_reserve:      CUDA0 compute buffer size =  3280.08 MiB
sched_reserve:  CUDA_Host compute buffer size =  2116.09 MiB
sched_reserve: graph nodes  = 2647
sched_reserve: graph splits = 2
sched_reserve: reserve took 857.64 ms, sched copies = 1
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:    356
clip_model_loader: n_kv:         23

clip_model_loader: has vision encoder
clip_ctx: CLIP using CUDA0 backend
load_hparams: projector:          gemma4v
load_hparams: n_embd:             1152
load_hparams: n_head:             16
load_hparams: n_ff:               4304
load_hparams: n_layer:            27
load_hparams: ffn_op:             gelu_quick
load_hparams: projection_dim:     2816

--- vision hparams ---
load_hparams: image_size:         224
load_hparams: patch_size:         16
load_hparams: has_llava_proj:     0
load_hparams: minicpmv_version:   0
load_hparams: n_merge:            3
load_hparams: n_wa_pattern: 0
load_hparams: image_min_pixels:   580608
load_hparams: image_max_pixels:   645120

load_hparams: model size:         1137.77 MiB
load_hparams: metadata size:      0.12 MiB
srv    load_model: loaded multimodal model, '/home/coder/models/gemma-4-26B-A4B-it-mmproj-f16.gguf'
srv    load_model: speculative decoding is not supported by multimodal, it will be disabled
srv    load_model: initializing slots, n_slots = 1
no implementations specified for speculative decoding
slot   load_model: id  0 | task -1 | speculative decoding context not initialized
slot   load_model: id  0 | task -1 | new slot, n_ctx = 262144
srv    load_model: prompt cache is enabled, size limit: 8192 MiB
srv    load_model: use `--cache-ram 0` to disable the prompt cache
srv    load_model: for more info see https://github.com/ggml-org/llama.cpp/pull/16391
init: chat template, example_format: '<bos><|turn>system
<|think|>You are a helpful assistant<turn|>
<|turn>user
Hello<turn|>
<|turn>model
Hi there<turn|>
<|turn>user
How are you?<turn|>
<|turn>model
'
srv          init: init: chat template, thinking = 1
main: model loaded
main: server is listening on http://0.0.0.0:5808
main: starting the main loop...
srv  update_slots: all slots are idle
srv  log_server_r: done request: GET / 127.0.0.1 200
srv  log_server_r: done request: GET /bundle.css 127.0.0.1 200
srv  log_server_r: done request: GET /bundle.js 127.0.0.1 200
srv  log_server_r: done request: HEAD /cors-proxy 127.0.0.1 404
srv  params_from_: Chat format: peg-native
slot get_availabl: id  0 | task -1 | selected slot by LRU, t_last = -1
slot launch_slot_: id  0 | task -1 | sampler chain: logits -> ?penalties -> ?dry -> ?top-n-sigma -> top-k -> ?typical -> top-p -> min-p -> ?xtc -> temp-ext -> dist 
slot launch_slot_: id  0 | task 0 | processing task, is_child = 0
slot update_slots: id  0 | task 0 | new prompt, n_ctx_slot = 262144, n_keep = 0, task.n_tokens = 20
slot update_slots: id  0 | task 0 | n_tokens = 0, memory_seq_rm [0, end)
slot update_slots: id  0 | task 0 | prompt processing progress, n_tokens = 16, batch.n_tokens = 16, progress = 0.800000
srv  log_server_r: done request: POST /v1/chat/completions 127.0.0.1 200
slot update_slots: id  0 | task 0 | n_tokens = 16, memory_seq_rm [16, end)
slot init_sampler: id  0 | task 0 | init sampler, took 0.01 ms, tokens: text = 20, total = 20
slot update_slots: id  0 | task 0 | prompt processing done, n_tokens = 20, batch.n_tokens = 4
reasoning-budget: activated, budget=2147483647 tokens
reasoning-budget: deactivated (natural end)
srv          stop: cancel task, id_task = 0
slot      release: id  0 | task 0 | stop processing: n_tokens = 1299, truncated = 0
srv  update_slots: all slots are idle
srv    operator(): operator(): cleaning up before exit...
llama_memory_breakdown_print: | memory breakdown [MiB] |  total    free     self   model   context   compute    unaccounted |
llama_memory_breakdown_print: |   - CUDA0 (GB10)       | 124610 = 56906 + (57150 = 48150 +    5720 +    3280) +       10554 |
llama_memory_breakdown_print: |   - Host               |                    3524 =  1408 +       0 +    2116                |