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Estimating GPU Memory Needed for LLMs
Tekinoroji
Nhungamiro yehunyanzvi
GPU Memory Management uye Fragmentation
Maitiro eAI maitiro ekugovera, kushandisazve, uye kudzoreredza ndangariro shoma paGPU, uye nei masara mapeji (kupatsanurwa) achigona kukonzera kunze-kwe-memory kukanganisa kunyangwe ndangariro zhinji dzichisara.
Teerera kugwaro iriAI-yakarondedzerwa · ~2 min
Pa peji ino2 min verenga
Pfupiso
Understanding it is key to fitting big models and avoiding mysterious crashes.
Kudzika Kwakadzika
GPU ndangariro yakagadziriswa uye yakakosha: kadhi rinogona kunge riine makumi maviri nemana, makumi masere, kana 192 GB yakazara, yakagovaniswa nehuremu hwemuenzaniso, ma activation, gradients, optimizer states, uye zvenguva pfupi buffers. Kufonera mutyairi kuti ape chiyeuchidzo pane zvese kushanda kwaizononoka, saka masisitimu akaita sePyTorch anoshandisa caching allocator inobata mabhuroko mahombe kumberi uye nekupa zvidimbu zvidimbu, wozochengeta zvidimbu zvakasunungurwa mudziva kuti zvishandiswezve. Kubata kuri kupatsanurwa: sezvo matensor ehukuru hwakasiyana akagoverwa uye kusunungurwa, nzvimbo yemahara inotsemuka kuita machunks akapararira. Iwe unogona kuve ne5 GB yemahara yakazara asi uchitadza kugovera inobatika 2 GB tensor nekuti hapana gap rimwe rakakura zvakakwana. Ichi ndicho chikonzero kudzidziswa kuchigona kuparara nekunze-kwe-memory kukanganisa kunyangwe ichiita senge iripo headroom.
Kuitwa muNyika yeChokwadi
A training team examines allocator statistics before testing expandable segments on a variable-batch workload.
A developer uses memory_summary and a memory snapshot to investigate active tensors and inactive split blocks.
An inference team uses vLLM’s paged KV-cache management to reduce cache-allocation waste.
A training team lowers batch size or checkpoints activations after confirming live tensor memory is the limiting factor.
Ramangwana reGPU Memory Management uye Fragmentation
Memory manejimendi iri kuve yakangwara uye yakawanda mapeji, yakafuridzirwa neanoshanda masisitimu. Tekinoroji senge virtual-memory-style allocators uye paged kutarisisa (inoshandiswa kubata iyo KV cache panguva yekufungidzira) inoderedza marara uye kupatsanuka zvakanyanya. Tarisira masisitimu ekusarudzika kuti awedzere, anokanganisa allocators, kuoneka zviri nani kuburikidza neakavakirwa-mukati maprofiles, uye yakasimba kubatana nekuburitsa uye kudzoreredza kuitira kuti sisitimu inojamba GPU, CPU, uye disk memory otomatiki kuchengetedza kushandiswa kwakakwira uye kubondera kusingawanzo.
Kuzvishandisa
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Chenjera
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Matanho anotevera
- Tsanangura latency, mhando, uye mutengo zvinangwa usati waitwa.
- Benchmark pasi pechokwadi mutoro uye data mamiriro.
- Chishandiso chekutarisa zvikanganiso, kudonha, uye mushandisi maitiro.
- Gadzirira nzira dzekudzosera kumashure uye dzezviitiko usati wawedzera.
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Take the GPU Memory Management and Fragmentation quiz
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Mibvunzo inowanzo bvunzwa
What is GPU Memory Management and Fragmentation?
Maitiro eAI maitiro ekugovera, kushandisazve, uye kudzoreredza ndangariro shoma paGPU, uye nei masara mapeji (kupatsanurwa) achigona kukonzera kunze-kwe-memory kukanganisa kunyangwe ndangariro zhinji dzichisara. Kuzvinzwisisa kwakakosha pakukodzera mamodheru makuru uye kudzivirira kuparara kusinganzwisisike.
Chii chinonzi GPU memory fragmentation?
Kupatsanurwa kunoreva ndangariro yemahara yakakwana, asi yakatsemuka kuita zvidimbu, saka hapana gaka rimwe chete rakakura zvakakwana kune hombe tensor.
Sei masisitimu akaita sePyTorch achishandisa caching memory allocator?
Kufonera cudaMalloc/cudaMahara kune yega yega kunononoka, saka caching allocator inobata mabhuroko makuru uye inoshandisa zvakare akasunungurwa kubva padziva.
A CUDA allocation fails despite apparently unused reserved memory. What should a developer investigate?
Allocator statistics and snapshots help distinguish fragmentation from live tensors or other allocations. The error alone does not prove a single cause.
Which PyTorch allocator option can be tested for changing-size workloads that leave unused memory slivers?
Expandable segments can reduce waste for selected allocation patterns. Availability and benefit depend on allocator configuration and workload.
Chii chinoita vLLM's PagedAttention inokwanisa kudzikisa tsvina yendangariro panguva yekufungidzira?
PejiAttention inochengetedza iyo KV cache mumapeji akagadziriswa-saizi senge OS chaiyo ndangariro, kucheka kupatsanuka uye kushumira zvikumbiro zvakawanda nemazvo.
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