OOM on Jetson Orin Nano and gemma-3n-E2B-it

[Fhrozen]

I am trying to launch a model with llama.cpp on a Jetson Orin Nano device, but I am getting an OOM error each time that I try to run with the full model.

I used the llama.cpp@03792ad, built with:

cmake -B build -DGGML_CUDA=ON
cmake --build build --config Release

Then, I tested llama-cli with:

llama-cli -hf unsloth/gemma-3n-E2B-it-GGUF:Q4_K_XL --n-gpu-layers 30

Using the whole model (31 layers) gave me this output:

ggml_cuda_init: GGML_CUDA_FORCE_MMQ:    no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
ggml_cuda_init: found 1 CUDA devices:
  Device 0: Orin, compute capability 8.7, VMM: yes
*   Trying 3.164.110.114:443...
* Connected to huggingface.co (3.164.110.114) port 443 (#0)
* ALPN, offering h2
* ALPN, offering http/1.1
*  CAfile: /etc/ssl/certs/ca-certificates.crt
*  CApath: /etc/ssl/certs
* SSL connection using TLSv1.3 / TLS_AES_128_GCM_SHA256
* ALPN, server accepted to use h2
* Server certificate:
*  subject: CN=huggingface.co
*  start date: Apr 13 00:00:00 2025 GMT
*  expire date: May 12 23:59:59 2026 GMT
*  subjectAltName: host "huggingface.co" matched cert's "huggingface.co"
*  issuer: C=US; O=Amazon; CN=Amazon RSA 2048 M02
*  SSL certificate verify ok.
* Using HTTP2, server supports multiplexing
* Connection state changed (HTTP/2 confirmed)
* Copying HTTP/2 data in stream buffer to connection buffer after upgrade: len=0
* Using Stream ID: 1 (easy handle 0xaaab01024d40)
> GET /v2/unsloth/gemma-3n-E2B-it-GGUF/manifests/Q4_K_XL HTTP/2
Host: huggingface.co
user-agent: llama-cpp
accept: application/json

* Connection state changed (MAX_CONCURRENT_STREAMS == 128)!
...
* Connection #0 to host huggingface.co left intact
common_download_file_single_online: using cached file: /home/nelson/.cache/llama.cpp/unsloth_gemma-3n-E2B-it-GGUF_gemma-3n-E2B-it-UD-Q4_K_XL.gguf
build: 6816 (03792ad9) with cc (Ubuntu 11.4.0-1ubuntu1~22.04.2) 11.4.0 for aarch64-linux-gnu
main: llama backend init
main: load the model and apply lora adapter, if any
llama_model_load_from_file_impl: using device CUDA0 (Orin) (0000:00:00.0) - 6687 MiB free
llama_model_loader: loaded meta data with 51 key-value pairs and 727 tensors from /home/nelson/.cache/llama.cpp/unsloth_gemma-3n-E2B-it-GGUF_gemma-3n-E2B-it-UD-Q4_K_XL.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              = gemma3n
llama_model_loader: - kv   1:                               general.type str              = model
llama_model_loader: - kv   2:                               general.name str              = Gemma-3N-E2B-It
llama_model_loader: - kv   3:                           general.finetune str              = 3n-E2B-it
llama_model_loader: - kv   4:                           general.basename str              = Gemma-3N-E2B-It
llama_model_loader: - kv   5:                       general.quantized_by str              = Unsloth
llama_model_loader: - kv   6:                         general.size_label str              = 4.5B
llama_model_loader: - kv   7:                            general.license str              = gemma
llama_model_loader: - kv   8:                           general.repo_url str              = https://huggingface.co/unsloth
llama_model_loader: - kv   9:                   general.base_model.count u32              = 1
llama_model_loader: - kv  10:                  general.base_model.0.name str              = Gemma 3n E2B It
llama_model_loader: - kv  11:          general.base_model.0.organization str              = Google
llama_model_loader: - kv  12:              general.base_model.0.repo_url str              = https://huggingface.co/google/gemma-3...
llama_model_loader: - kv  13:                               general.tags arr[str,6]       = ["automatic-speech-recognition", "uns...
llama_model_loader: - kv  14:                     gemma3n.context_length u32              = 32768
llama_model_loader: - kv  15:                   gemma3n.embedding_length u32              = 2048
llama_model_loader: - kv  16:                        gemma3n.block_count u32              = 30
llama_model_loader: - kv  17:                gemma3n.feed_forward_length arr[i32,30]      = [8192, 8192, 8192, 8192, 8192, 8192, ...
llama_model_loader: - kv  18:               gemma3n.attention.head_count u32              = 8
llama_model_loader: - kv  19:   gemma3n.attention.layer_norm_rms_epsilon f32              = 0.000001
llama_model_loader: - kv  20:               gemma3n.attention.key_length u32              = 256
llama_model_loader: - kv  21:             gemma3n.attention.value_length u32              = 256
llama_model_loader: - kv  22:                     gemma3n.rope.freq_base f32              = 1000000.000000
llama_model_loader: - kv  23:           gemma3n.attention.sliding_window u32              = 512
llama_model_loader: - kv  24:            gemma3n.attention.head_count_kv u32              = 2
llama_model_loader: - kv  25:                   gemma3n.altup.active_idx u32              = 0
llama_model_loader: - kv  26:                   gemma3n.altup.num_inputs u32              = 4
llama_model_loader: - kv  27:   gemma3n.embedding_length_per_layer_input u32              = 256
llama_model_loader: - kv  28:         gemma3n.attention.shared_kv_layers u32              = 10
llama_model_loader: - kv  29:          gemma3n.activation_sparsity_scale arr[f32,30]      = [1.644854, 1.644854, 1.644854, 1.6448...
llama_model_loader: - kv  30:   gemma3n.attention.sliding_window_pattern arr[bool,30]     = [true, true, true, true, false, true,...
llama_model_loader: - kv  31:                    tokenizer.chat_template str              = {{ bos_token }}\n{%- if messages[0]['r...
llama_model_loader: - kv  32:                       tokenizer.ggml.model str              = llama
llama_model_loader: - kv  33:                         tokenizer.ggml.pre str              = default
llama_model_loader: - kv  34:                      tokenizer.ggml.tokens arr[str,262144]  = ["<pad>", "<eos>", "<bos>", "<unk>", ...
llama_model_loader: - kv  35:                      tokenizer.ggml.scores arr[f32,262144]  = [-1000.000000, -1000.000000, -1000.00...
llama_model_loader: - kv  36:                  tokenizer.ggml.token_type arr[i32,262144]  = [3, 3, 3, 3, 3, 4, 3, 3, 3, 3, 3, 3, ...
llama_model_loader: - kv  37:                tokenizer.ggml.bos_token_id u32              = 2
llama_model_loader: - kv  38:                tokenizer.ggml.eos_token_id u32              = 106
llama_model_loader: - kv  39:            tokenizer.ggml.unknown_token_id u32              = 3
llama_model_loader: - kv  40:            tokenizer.ggml.padding_token_id u32              = 0
llama_model_loader: - kv  41:               tokenizer.ggml.add_bos_token bool             = true
llama_model_loader: - kv  42:               tokenizer.ggml.add_sep_token bool             = false
llama_model_loader: - kv  43:               tokenizer.ggml.add_eos_token bool             = false
llama_model_loader: - kv  44:            tokenizer.ggml.add_space_prefix bool             = false
llama_model_loader: - kv  45:               general.quantization_version u32              = 2
llama_model_loader: - kv  46:                          general.file_type u32              = 15
llama_model_loader: - kv  47:                      quantize.imatrix.file str              = gemma-3n-E2B-it-GGUF/imatrix_unsloth.dat
llama_model_loader: - kv  48:                   quantize.imatrix.dataset str              = unsloth_calibration_gemma-3n-E2B-it.txt
llama_model_loader: - kv  49:             quantize.imatrix.entries_count u32              = 400
llama_model_loader: - kv  50:              quantize.imatrix.chunks_count u32              = 1326
llama_model_loader: - type  f32:  362 tensors
llama_model_loader: - type  f16:   93 tensors
llama_model_loader: - type q8_0:    1 tensors
llama_model_loader: - type q4_K:  156 tensors
llama_model_loader: - type q5_K:   56 tensors
llama_model_loader: - type q6_K:   43 tensors
llama_model_loader: - type iq4_xs:   16 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type   = Q4_K - Medium
print_info: file size   = 3.49 GiB (6.73 BPW)
load: printing all EOG tokens:
load:   - 106 ('<end_of_turn>')
load: special tokens cache size = 6414
load: token to piece cache size = 1.9446 MB
print_info: arch             = gemma3n
print_info: vocab_only       = 0
print_info: n_ctx_train      = 32768
print_info: n_embd           = 2048
print_info: n_layer          = 30
print_info: n_head           = 8
print_info: n_head_kv        = 2
print_info: n_rot            = 256
print_info: n_swa            = 512
print_info: is_swa_any       = 1
print_info: n_embd_head_k    = 256
print_info: n_embd_head_v    = 256
print_info: n_gqa            = 4
print_info: n_embd_k_gqa     = 512
print_info: n_embd_v_gqa     = 512
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             = 8192
print_info: n_expert         = 0
print_info: n_expert_used    = 0
print_info: causal attn      = 1
print_info: pooling type     = 0
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: n_ctx_orig_yarn  = 32768
print_info: rope_finetuned   = unknown
print_info: model type       = E2B
print_info: model params     = 4.46 B
print_info: general.name     = Gemma-3N-E2B-It
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        = 106 '<end_of_turn>'
print_info: EOT token        = 106 '<end_of_turn>'
print_info: UNK token        = 3 '<unk>'
print_info: PAD token        = 0 '<pad>'
print_info: LF token         = 248 '<0x0A>'
print_info: EOG token        = 106 '<end_of_turn>'
print_info: max token length = 48
load_tensors: loading model tensors, this can take a while... (mmap = true)
NvMapMemAllocInternalTagged: 1075072515 error 12
NvMapMemHandleAlloc: error 0
NvMapMemAllocInternalTagged: 1075072515 error 12
NvMapMemHandleAlloc: error 0
ggml_backend_cuda_buffer_type_alloc_buffer: allocating 3573.76 MiB on device 0: cudaMalloc failed: out of memory
alloc_tensor_range: failed to allocate CUDA0 buffer of size 3747356416
llama_model_load: error loading model: unable to allocate CUDA0 buffer
llama_model_load_from_file_impl: failed to load model
common_init_from_params: failed to load model '/home/nelson/.cache/llama.cpp/unsloth_gemma-3n-E2B-it-GGUF_gemma-3n-E2B-it-UD-Q4_K_XL.gguf', try reducing --n-gpu-layers if you're running out of VRAM
main: error: unable to load model

As you read, the device has device CUDA0 (Orin) (0000:00:00.0) - 6687 MiB free and the model would only require 3573.76 MiB.

If someone had the same problem, could you guide me through? Also, I tried jetson-containers, but it seems outdated (got no gemma-3n model available error).

BTW, I also tried using GGML_CUDA_ENABLE_UNIFIED_MEMORY=1 llama-cli but got the same error. (Not sure if the unified memory is working, I have a swap memory of 128Gb)