{"id":24630,"date":"2026-05-25T09:39:22","date_gmt":"2026-05-25T02:39:22","guid":{"rendered":"https:\/\/vnso.vn\/?p=24630"},"modified":"2026-05-25T09:44:09","modified_gmt":"2026-05-25T02:44:09","slug":"structural-sparsity-la-gi","status":"publish","type":"post","link":"http:\/\/vnso.vn\/en\/structural-sparsity-la-gi\/","title":{"rendered":"Structural Sparsity l\u00e0 g\u00ec? C\u00e1ch t\u0103ng hi\u1ec7u su\u1ea5t Model AI g\u1ea5p 2 l\u1ea7n"},"content":{"rendered":"

C\u00e1c m\u00f4 h\u00ecnh AI hi\u1ec7n \u0111\u1ea1i \u0111ang ph\u00e1t tri\u1ec3n v\u1edbi t\u1ed1c \u0111\u1ed9 r\u1ea5t nhanh v\u00e0 c\u00f3 th\u1ec3 ch\u1ee9a h\u00e0ng ch\u1ee5c t\u1ef7, th\u1eadm ch\u00ed h\u00e0ng ngh\u00ecn t\u1ef7 tham s\u1ed1. \u0110i\u1ec1u n\u00e0y k\u00e9o theo nhu c\u1ea7u c\u1ef1c l\u1edbn v\u1ec1 GPU, VRAM, \u0111i\u1ec7n n\u0103ng v\u00e0 h\u1ea1 t\u1ea7ng AI Data Center. \u0110\u00e2y c\u0169ng l\u00e0 l\u00fd do v\u00ec sao c\u00e1c GPU AI nh\u01b0 NVIDIA A100 hay NVIDIA H100 \u0111ang t\u00edch h\u1ee3p th\u00eam nhi\u1ec1u c\u00f4ng ngh\u1ec7 t\u1ed1i \u01b0u nh\u01b0\u00a0Structural Sparsity<\/strong> nh\u1eb1m gi\u00fap AI inference nhanh h\u01a1n v\u00e0 ti\u1ebft ki\u1ec7m t\u00e0i nguy\u00ean h\u01a1n.<\/p>\n

L\u00e0m sao \u0111\u1ec3 m\u00f4 h\u00ecnh AI c\u1ee7a b\u1ea1n ch\u1ea1y nhanh g\u1ea5p \u0111\u00f4i tr\u00ean GPU m\u00e0 kh\u00f4ng c\u1ea7n n\u00e2ng c\u1ea5p ph\u1ea7n c\u1ee9ng hay hy sinh \u0111\u1ed9 ch\u00ednh x\u00e1c? C\u00f9ng VNSO t\u00ecm hi\u1ec3u t\u1ea5t c\u1ea3 v\u1ec1 Structural Sparsity t\u1eeb A-Z.<\/p>\n

Structural Sparsity l\u00e0 g\u00ec?<\/h2>\n

Structural Sparsity (t\u1ea1m d\u1ecbch l\u00e0 T\u00ednh th\u01b0a c\u00f3 c\u1ea5u tr\u00fac) l\u00e0 k\u1ef9 thu\u1eadt t\u1ed1i \u01b0u AI model b\u1eb1ng c\u00e1ch \u0111\u01b0a m\u1ed9t ph\u1ea7n tr\u1ecdng s\u1ed1 c\u1ee7a m\u1ea1ng neural v\u1ec1 gi\u00e1 tr\u1ecb 0 theo m\u1ed9t c\u1ea5u tr\u00fac c\u1ed1 \u0111\u1ecbnh. Khi \u0111\u00f3, GPU c\u00f3 th\u1ec3 b\u1ecf qua c\u00e1c ph\u00e9p t\u00ednh li\u00ean quan t\u1edbi nh\u1eefng gi\u00e1 tr\u1ecb n\u00e0y \u0111\u1ec3 gi\u1ea3m l\u01b0\u1ee3ng compute c\u1ea7n x\u1eed l\u00fd.<\/p>\n

Trong neural network (M\u1ea1ng n\u01a1-ron nh\u00e2n t\u1ea1o)<\/strong>, kh\u00f4ng ph\u1ea3i m\u1ecdi tr\u1ecdng s\u1ed1 \u0111\u1ec1u c\u00f3 m\u1ee9c \u1ea3nh h\u01b0\u1edfng gi\u1ed1ng nhau. Nhi\u1ec1u nghi\u00ean c\u1ee9u cho th\u1ea5y m\u1ed9t l\u01b0\u1ee3ng l\u1edbn parameter b\u00ean trong model th\u1ef1c t\u1ebf \u0111\u00f3ng g\u00f3p r\u1ea5t \u00edt v\u00e0o k\u1ebft qu\u1ea3 cu\u1ed1i c\u00f9ng. Structural Sparsity t\u1eadn d\u1ee5ng \u0111\u1eb7c \u0111i\u1ec3m n\u00e0y \u0111\u1ec3 lo\u1ea1i b\u1ecf c\u00e1c ph\u00e9p t\u00ednh d\u01b0 th\u1eeba.<\/p>\n

Thay v\u00ec x\u1eed l\u00fd to\u00e0n b\u1ed9 ma tr\u1eadn d\u1eef li\u1ec7u \u1edf d\u1ea1ng dense matrix, GPU s\u1ebd ch\u1ec9 t\u1eadp trung v\u00e0o nh\u1eefng ph\u1ea7n d\u1eef li\u1ec7u c\u00f2n th\u1ef1c s\u1ef1 quan tr\u1ecdng.<\/p>\n

V\u00ed d\u1ee5:<\/p>\n

Dense matrix:<\/strong><\/p>\n

[0.5 0.2 0.9 0.7]\r\n[0.3 0.6 0.1 0.8]\r\n<\/code><\/pre>\n
Sau khi \u00e1p d\u1ee5ng sparsity:<\/strong><\/p>\n
[0.5 0   0.9 0]\r\n[0   0.6 0   0.8]\r\n<\/code><\/pre>\n
Nh\u1eefng v\u1ecb tr\u00ed b\u1eb1ng 0 s\u1ebd \u0111\u01b0\u1ee3c GPU b\u1ecf qua trong qu\u00e1 tr\u00ecnh x\u1eed l\u00fd. \u0110i\u1ec1u n\u00e0y gi\u00fap gi\u1ea3m \u0111\u00e1ng k\u1ec3 s\u1ed1 ph\u00e9p nh\u00e2n ma tr\u1eadn, v\u1ed1n l\u00e0 t\u00e1c v\u1ee5 ti\u00eau t\u1ed1n nhi\u1ec1u t\u00e0i nguy\u00ean nh\u1ea5t trong AI.Theo NVIDIA Developer Blog, Structured Sparsity c\u00f3 th\u1ec3 gi\u00fap t\u0103ng throughput AI inference l\u00ean t\u1edbi 2 l\u1ea7n trong \u0111i\u1ec1u ki\u1ec7n l\u00fd t\u01b0\u1edfng tr\u00ean ki\u1ebfn tr\u00fac NVIDIA Ampere.<\/p>\n
>>> Xem th\u00eam NVIDIA A100 l\u00e0 g\u00ec? Ph\u00e2n t\u00edch chi ti\u1ebft t\u1eeb A-Z<\/a><\/strong><\/p>\n
\"Structural<\/a><\/h2>\n
Structural Sparsity ho\u1ea1t \u0111\u1ed9ng nh\u01b0 th\u1ebf n\u00e0o?<\/h2>\n
\u0110i\u1ec3m quan tr\u1ecdng nh\u1ea5t c\u1ee7a Structural Sparsity n\u1eb1m \u1edf t\u00ednh \u201cc\u00f3 c\u1ea5u tr\u00fac\u201d. Trong AI, sparsity th\u01b0\u1eddng \u0111\u01b0\u1ee3c chia th\u00e0nh hai h\u01b0\u1edbng ch\u00ednh g\u1ed3m Unstructured Sparsity v\u00e0 Structured Sparsity. <\/strong>V\u1edbi Unstructured Sparsity, c\u00e1c gi\u00e1 tr\u1ecb 0 \u0111\u01b0\u1ee3c t\u1ea1o ra ng\u1eabu nhi\u00ean b\u00ean trong ma tr\u1eadn. C\u00e1ch n\u00e0y c\u00f3 th\u1ec3 lo\u1ea1i b\u1ecf r\u1ea5t nhi\u1ec1u parameter nh\u01b0ng l\u1ea1i kh\u00f3 t\u1ed1i \u01b0u tr\u00ean GPU do d\u1eef li\u1ec7u ph\u00e2n b\u1ed1 kh\u00f4ng \u0111\u1ed3ng \u0111\u1ec1u.<\/p>\n
GPU hi\u1ec7n \u0111\u1ea1i \u0111\u01b0\u1ee3c thi\u1ebft k\u1ebf \u0111\u1ec3 x\u1eed l\u00fd song song v\u1edbi c\u00e1c pattern (c\u1ea5u tr\u00fac) d\u1eef li\u1ec7u r\u00f5 r\u00e0ng. N\u1ebfu d\u1eef li\u1ec7u qu\u00e1 ng\u1eabu nhi\u00ean, hi\u1ec7u qu\u1ea3 t\u0103ng t\u1ed1c th\u1ef1c t\u1ebf s\u1ebd th\u1ea5p h\u01a1n r\u1ea5t nhi\u1ec1u so v\u1edbi l\u00fd thuy\u1ebft.<\/p>\n
C\u1ea5u tr\u00fac 2:4<\/h3>\n
Structural Sparsity gi\u1ea3i quy\u1ebft v\u1ea5n \u0111\u1ec1 n\u00e0y b\u1eb1ng c\u00e1ch \u00e1p d\u1ee5ng m\u1ed9t pattern c\u1ed1 \u0111\u1ecbnh. Pattern ph\u1ed5 bi\u1ebfn nh\u1ea5t hi\u1ec7n nay l\u00e0 2:4 sparsity<\/strong>. \u0110i\u1ec1u n\u00e0y c\u00f3 ngh\u0129a l\u00e0 trong m\u1ed7i block g\u1ed3m 4 tr\u1ecdng s\u1ed1, ch\u1ec9 c\u00f3 2 gi\u00e1 tr\u1ecb \u0111\u01b0\u1ee3c gi\u1eef l\u1ea1i, c\u00f2n 2 gi\u00e1 tr\u1ecb c\u00f2n l\u1ea1i s\u1ebd \u0111\u01b0\u1ee3c \u0111\u01b0a v\u1ec1 0.<\/p>\n
V\u00ed d\u1ee5:<\/strong><\/p>\n
[0.8 0 0.5 0]\r\n<\/code><\/pre>\n
GPU c\u00f3 th\u1ec3 nh\u1eadn di\u1ec7n ch\u00ednh x\u00e1c c\u1ea5u tr\u00fac n\u00e0y \u0111\u1ec3 b\u1ecf qua m\u1ed9t n\u1eeda l\u01b0\u1ee3ng ph\u00e9p t\u00ednh c\u1ea7n th\u1ef1c hi\u1ec7n. \u0110\u00e2y ch\u00ednh l\u00e0 n\u1ec1n t\u1ea3ng c\u1ee7a Sparse Tensor Core tr\u00ean c\u00e1c GPU NVIDIA th\u1ebf h\u1ec7 m\u1edbi.<\/p>\n
Sparse Tensor Core xu\u1ea5t hi\u1ec7n l\u1ea7n \u0111\u1ea7u tr\u00ean ki\u1ebfn tr\u00fac Ampere v\u00e0 hi\u1ec7n \u0111\u01b0\u1ee3c t\u00edch h\u1ee3p tr\u00ean nhi\u1ec1u d\u00f2ng GPU AI nh\u01b0 A100, H100, RTX 30 series, RTX 40 series v\u00e0 c\u1ea3 ki\u1ebfn tr\u00fac Blackwell m\u1edbi nh\u1ea5t.<\/p>\n
Tensor Core th\u00f4ng th\u01b0\u1eddng s\u1ebd x\u1eed l\u00fd to\u00e0n b\u1ed9 ma tr\u1eadn d\u1eef li\u1ec7u. Trong khi \u0111\u00f3, Sparse Tensor Core ch\u1ec9 t\u1eadp trung x\u1eed l\u00fd c\u00e1c non-zero values c\u00f9ng metadata m\u00f4 t\u1ea3 v\u1ecb tr\u00ed d\u1eef li\u1ec7u b\u00ean trong ma tr\u1eadn. \u0110i\u1ec1u n\u00e0y gi\u00fap gi\u1ea3m \u0111\u00e1ng k\u1ec3 l\u01b0\u1ee3ng compute th\u1ef1c t\u1ebf m\u00e0 GPU ph\u1ea3i x\u1eed l\u00fd.<\/p>\n
T\u1ea1i sao c\u1ea7n c\u00f4ng ngh\u1ec7 Structural Sparsity?<\/h2>\n
Khi AI model ng\u00e0y c\u00e0ng l\u1edbn, chi ph\u00ed inference \u0111ang t\u0103ng r\u1ea5t nhanh. M\u1ed9t m\u00f4 h\u00ecnh AI hi\u1ec7n \u0111\u1ea1i c\u00f3 th\u1ec3 y\u00eau c\u1ea7u h\u00e0ng tr\u0103m GB VRAM, h\u00e0ng ngh\u00ecn TFLOPS compute v\u00e0 m\u1ee9c \u0111i\u1ec7n n\u0103ng ti\u00eau th\u1ee5 c\u1ef1c cao. \u0110i\u1ec1u n\u00e0y t\u1ea1o \u00e1p l\u1ef1c l\u1edbn cho AI Data Center v\u00e0 h\u1ec7 th\u1ed1ng Cloud GPU.<\/p>\n
V\u00ed d\u1ee5 nh\u01b0 GPU NVIDIA H100 c\u00f3 th\u1ec3 \u0111\u1ea1t hi\u1ec7u su\u1ea5t Sparse Tensor l\u00ean t\u1edbi h\u00e0ng ngh\u00ecn TFLOPS khi s\u1eed d\u1ee5ng sparsity k\u1ebft h\u1ee3p Tensor Core.<\/p>\n
N\u1ebfu ch\u1ec9 t\u0103ng hi\u1ec7u n\u0103ng b\u1eb1ng c\u00e1ch m\u1edf r\u1ed9ng ph\u1ea7n c\u1ee9ng, chi ph\u00ed v\u1eadn h\u00e0nh s\u1ebd t\u0103ng r\u1ea5t nhanh. Structural Sparsity gi\u00fap gi\u1ea3i quy\u1ebft v\u1ea5n \u0111\u1ec1 n\u00e0y b\u1eb1ng c\u00e1ch gi\u1ea3m s\u1ed1 ph\u00e9p t\u00ednh ngay t\u1eeb b\u00ean trong model thay v\u00ec ph\u1ee5 thu\u1ed9c ho\u00e0n to\u00e0n v\u00e0o vi\u1ec7c b\u1ed5 sung GPU.<\/p>\n
Nh\u1edd gi\u1ea3m l\u01b0\u1ee3ng compute c\u1ea7n x\u1eed l\u00fd, AI inference c\u00f3 th\u1ec3 \u0111\u1ea1t latency th\u1ea5p h\u01a1n, throughput cao h\u01a1n v\u00e0 ti\u00eau th\u1ee5 \u00edt \u0111i\u1ec7n n\u0103ng h\u01a1n. \u0110\u1ed3ng th\u1eddi, \u00e1p l\u1ef1c l\u00ean VRAM bandwidth c\u0169ng gi\u1ea3m \u0111\u00e1ng k\u1ec3, cho ph\u00e9p c\u00f9ng m\u1ed9t GPU x\u1eed l\u00fd nhi\u1ec1u request AI h\u01a1n trong c\u00f9ng kho\u1ea3ng th\u1eddi gian. \u0110\u1ed1i v\u1edbi doanh nghi\u1ec7p tri\u1ec3n khai AI tr\u00ean quy m\u00f4 l\u1edbn, \u0111\u00e2y l\u00e0 y\u1ebfu t\u1ed1 r\u1ea5t quan tr\u1ecdng v\u00ec chi ph\u00ed GPU hi\u1ec7n nay v\u1eabn \u1edf m\u1ee9c r\u1ea5t cao.<\/p>\n
>>> Xem th\u00eam AI Data Center l\u00e0 g\u00ec? T\u1ea5t c\u1ea3 nh\u1eefng g\u00ec b\u1ea1n c\u1ea7n bi\u1ebft<\/a><\/strong><\/p>\n
\"T\u1ea1i<\/a><\/h3>\n
L\u1ee3i \u00edch th\u1ef1c t\u1ebf<\/h3>\n
M\u1ed9t trong nh\u1eefng l\u1ee3i \u00edch l\u1edbn nh\u1ea5t c\u1ee7a Structural Sparsity l\u00e0 kh\u1ea3 n\u0103ng t\u0103ng t\u1ed1c AI inference m\u00e0 kh\u00f4ng c\u1ea7n thay \u0111\u1ed5i to\u00e0n b\u1ed9 h\u1ea1 t\u1ea7ng ph\u1ea7n c\u1ee9ng. Theo NVIDIA, m\u00f4 h\u00ecnh \u00e1p d\u1ee5ng 2:4 sparsity c\u00f3 th\u1ec3 \u0111\u1ea1t throughput cao h\u01a1n kho\u1ea3ng 30% t\u1edbi 100%<\/strong> t\u00f9y workload th\u1ef1c t\u1ebf.<\/p>\n
Hi\u1ec7u qu\u1ea3 n\u00e0y \u0111\u1eb7c bi\u1ec7t r\u00f5 r\u1ec7t trong c\u00e1c t\u00e1c v\u1ee5 AI c\u00f3 kh\u1ed1i l\u01b0\u1ee3ng matrix multiplication r\u1ea5t l\u1edbn nh\u01b0 LLM inference, Computer Vision, Recommendation Systems hay Generative AI<\/strong>. Ngo\u00e0i hi\u1ec7u n\u0103ng, sparsity c\u00f2n gi\u00fap t\u1ed1i \u01b0u \u0111i\u1ec7n n\u0103ng ti\u00eau th\u1ee5. \u0110\u00e2y \u0111ang l\u00e0 b\u00e0i to\u00e1n c\u1ef1c k\u1ef3 l\u1edbn trong ng\u00e0nh AI hi\u1ec7n nay khi nhu c\u1ea7u compute li\u00ean t\u1ee5c t\u0103ng m\u1ea1nh.<\/p>\n
Theo IEA Energy and AI Report, \u0111i\u1ec7n n\u0103ng ti\u00eau th\u1ee5 c\u1ee7a AI Data Center \u0111ang t\u0103ng nhanh do s\u1ef1 b\u00f9ng n\u1ed5 c\u1ee7a training v\u00e0 inference AI. Nh\u1eefng c\u00f4ng ngh\u1ec7 gi\u00fap gi\u1ea3m compute workload nh\u01b0 sparsity v\u00e0 quantization \u0111ang tr\u1edf th\u00e0nh h\u01b0\u1edbng t\u1ed1i \u01b0u quan tr\u1ecdng nh\u1eb1m c\u1ea3i thi\u1ec7n hi\u1ec7u su\u1ea5t\/watt \u0111i\u1ec7n.<\/p>\n
Structural Sparsity c\u0169ng mang l\u1ea1i l\u1ee3i \u00edch l\u1edbn cho d\u1ecbch v\u1ee5 Cloud GPU<\/strong>. Khi inference nhanh h\u01a1n, c\u00f9ng m\u1ed9t GPU c\u00f3 th\u1ec3 ph\u1ee5c v\u1ee5 nhi\u1ec1u ng\u01b0\u1eddi d\u00f9ng h\u01a1n, t\u1eeb \u0111\u00f3 gi\u00fap doanh nghi\u1ec7p t\u1ed1i \u01b0u chi ph\u00ed v\u1eadn h\u00e0nh AI.<\/p>\n
\u0110i\u1ec3m h\u1ea1n ch\u1ebf c\u1ee7a Structural Sparsity (\u0110\u1ed9 ch\u00ednh x\u00e1c)<\/h2>\n
N\u1ebfu lo\u1ea1i b\u1ecf qu\u00e1 nhi\u1ec1u tr\u1ecdng s\u1ed1, m\u00f4 h\u00ecnh AI c\u00f3 th\u1ec3 b\u1ecb gi\u1ea3m accuracy (\u0111\u1ed9 ch\u00ednh x\u00e1c), suy gi\u1ea3m reasoning ability (kh\u1ea3 n\u0103ng suy lu\u1eadn) ho\u1eb7c t\u0103ng hallucination (AI \u1ea3o gi\u00e1c). M\u1ed9t s\u1ed1 model c\u00f2n c\u00f3 th\u1ec3 m\u1ea5t kh\u1ea3 n\u0103ng t\u1ed5ng qu\u00e1t h\u00f3a n\u1ebfu sparsity \u0111\u01b0\u1ee3c \u00e1p d\u1ee5ng qu\u00e1 m\u1ea1nh.<\/p>\n
V\u00ec v\u1eady, Structural Sparsity kh\u00f4ng \u0111\u01a1n gi\u1ea3n l\u00e0 vi\u1ec7c \u201cx\u00f3a b\u1edbt parameter\u201d. C\u00e1c framework AI hi\u1ec7n \u0111\u1ea1i th\u01b0\u1eddng ph\u1ea3i fine-tune l\u1ea1i model sau pruning nh\u1eb1m c\u00e2n b\u1eb1ng gi\u1eefa t\u1ed1c \u0111\u1ed9 v\u00e0 \u0111\u1ed9 ch\u00ednh x\u00e1c.<\/p>\n
Theo nghi\u00ean c\u1ee9u t\u1eeb Microsoft Research SlideSparse, nhi\u1ec1u m\u00f4 h\u00ecnh reasoning hi\u1ec7n \u0111\u1ea1i c\u00f3 th\u1ec3 gi\u1ea3m hi\u1ec7u n\u0103ng \u0111\u00e1ng k\u1ec3 n\u1ebfu \u00e1p d\u1ee5ng strict 50% sparsity tr\u00ean to\u00e0n b\u1ed9 network. \u0110\u00e2y c\u0169ng l\u00e0 l\u00fd do ng\u00e0nh AI hi\u1ec7n nay \u0111ang nghi\u00ean c\u1ee9u nhi\u1ec1u h\u01b0\u1edbng m\u1edbi nh\u01b0 adaptive sparsity, dynamic sparsity hay flexible sparsity pattern nh\u1eb1m gi\u1eef accuracy t\u1ed1t h\u01a1n trong khi v\u1eabn t\u0103ng t\u1ed1c inference.<\/p>\n
Structural Sparsity \u0111\u01b0\u1ee3c \u1ee9ng d\u1ee5ng \u1edf \u0111\u00e2u?<\/h2>\n
Structural Sparsity hi\u1ec7n xu\u1ea5t hi\u1ec7n trong r\u1ea5t nhi\u1ec1u h\u1ec7 th\u1ed1ng AI hi\u1ec7n \u0111\u1ea1i.<\/p>\n
Trong AI inference<\/strong>, sparsity gi\u00fap chatbot AI ph\u1ea3n h\u1ed3i nhanh h\u01a1n v\u00e0 gi\u1ea3m chi ph\u00ed v\u1eadn h\u00e0nh GPU. V\u1edbi c\u00e1c m\u00f4 h\u00ecnh LLM, \u0111\u00e2y l\u00e0 y\u1ebfu t\u1ed1 c\u1ef1c k\u1ef3 quan tr\u1ecdng v\u00ec m\u1ed7i token sinh ra \u0111\u1ec1u y\u00eau c\u1ea7u l\u01b0\u1ee3ng compute r\u1ea5t l\u1edbn.<\/p>\n
Trong Computer Vision<\/strong>, sparsity gi\u00fap t\u0103ng t\u1ed1c object detection v\u00e0 image recognition tr\u00ean quy m\u00f4 l\u1edbn. \u0110\u1ed1i v\u1edbi recommendation systems c\u1ee7a c\u00e1c n\u1ec1n t\u1ea3ng l\u1edbn nh\u01b0 TikTok, YouTube, Netflix hay Amazon, sparsity gi\u00fap gi\u1ea3m t\u1ea3i compute cho recommendation engine khi ph\u1ea3i ph\u1ee5c v\u1ee5 h\u00e0ng tri\u1ec7u ng\u01b0\u1eddi d\u00f9ng \u0111\u1ed3ng th\u1eddi.<\/p>\n
Ngo\u00e0i AI Data Center<\/strong>, sparsity c\u00f2n r\u1ea5t quan tr\u1ecdng v\u1edbi Edge AI v\u00e0 AI on-device, n\u01a1i ph\u1ea7n c\u1ee9ng b\u1ecb gi\u1edbi h\u1ea1n v\u1ec1 \u0111i\u1ec7n n\u0103ng v\u00e0 t\u00e0i nguy\u00ean x\u1eed l\u00fd.<\/p>\n
Structural Sparsity, Unstructured Sparsity v\u00e0 Quantization kh\u00e1c nhau nh\u01b0 th\u1ebf n\u00e0o?<\/h2>\n\n\n\n\n\n\n\n
C\u00f4ng ngh\u1ec7<\/th>\nC\u00e1ch ho\u1ea1t \u0111\u1ed9ng<\/th>\nM\u1ee5c ti\u00eau ch\u00ednh<\/th>\n\u01afu \u0111i\u1ec3m<\/th>\nH\u1ea1n ch\u1ebf<\/th>\n<\/tr>\n<\/thead>\n
Structural Sparsity<\/td>\n\u0110\u01b0a m\u1ed9t ph\u1ea7n tr\u1ecdng s\u1ed1 v\u1ec1 0 theo pattern c\u1ed1 \u0111\u1ecbnh nh\u01b0 2:4<\/td>\nGi\u1ea3m s\u1ed1 ph\u00e9p t\u00ednh GPU ph\u1ea3i x\u1eed l\u00fd<\/td>\nGPU t\u0103ng t\u1ed1c th\u1ef1c t\u1ebf t\u1ed1t, t\u1ed1i \u01b0u cho Tensor Core<\/td>\nC\u00f3 th\u1ec3 \u1ea3nh h\u01b0\u1edfng accuracy n\u1ebfu sparsity qu\u00e1 cao<\/td>\n<\/tr>\n
Unstructured Sparsity<\/td>\n\u0110\u01b0a tr\u1ecdng s\u1ed1 v\u1ec1 0 ng\u1eabu nhi\u00ean<\/td>\nGi\u1ea3m s\u1ed1 l\u01b0\u1ee3ng parameter<\/td>\nC\u00f3 th\u1ec3 gi\u1ea3m model size r\u1ea5t m\u1ea1nh<\/td>\nKh\u00f3 t\u1ed1i \u01b0u tr\u00ean GPU do d\u1eef li\u1ec7u ph\u00e2n m\u1ea3nh<\/td>\n<\/tr>\n
Quantization<\/td>\nGi\u1ea3m \u0111\u1ed9 ch\u00ednh x\u00e1c d\u1eef li\u1ec7u t\u1eeb FP32 xu\u1ed1ng FP16, INT8, FP8<\/td>\nGi\u1ea3m dung l\u01b0\u1ee3ng d\u1eef li\u1ec7u v\u00e0 compute<\/td>\nGi\u1ea3m VRAM, t\u0103ng t\u1ed1c inference, ti\u1ebft ki\u1ec7m \u0111i\u1ec7n<\/td>\nC\u00f3 th\u1ec3 gi\u1ea3m \u0111\u1ed9 ch\u00ednh x\u00e1c model<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
Quantization gi\u1ed1ng nh\u01b0 vi\u1ec7c gi\u1ea3m ch\u1ea5t l\u01b0\u1ee3ng file \u1ea3nh t\u1eeb r\u1ea5t chi ti\u1ebft xu\u1ed1ng m\u1ee9c nh\u1eb9 h\u01a1n \u0111\u1ec3 ti\u1ebft ki\u1ec7m dung l\u01b0\u1ee3ng nh\u01b0ng v\u1eabn \u0111\u1ee7 nh\u00ecn r\u00f5.<\/p>\n
Unstructured Sparsity gi\u1ed1ng nh\u01b0 x\u00f3a ng\u1eabu nhi\u00ean m\u1ed9t s\u1ed1 chi ti\u1ebft trong b\u1ee9c \u1ea3nh. File c\u00f3 th\u1ec3 nh\u1eb9 h\u01a1n nh\u01b0ng d\u1eef li\u1ec7u b\u1ecb r\u1eddi r\u1ea1c n\u00ean GPU kh\u00f3 x\u1eed l\u00fd hi\u1ec7u qu\u1ea3.<\/p>\n
Structural Sparsity th\u00ec kh\u00e1c. C\u00f4ng ngh\u1ec7 n\u00e0y lo\u1ea1i b\u1ecf d\u1eef li\u1ec7u theo m\u1ed9t quy lu\u1eadt c\u1ed1 \u0111\u1ecbnh \u0111\u1ec3 GPU d\u1ec5 nh\u1eadn bi\u1ebft v\u00e0 b\u1ecf qua c\u00e1c ph\u1ea7n kh\u00f4ng c\u1ea7n t\u00ednh to\u00e1n.<\/p>\n
T\u01b0\u01a1ng lai c\u1ee7a Structural Sparsity<\/h2>\n
AI hi\u1ec7n nay kh\u00f4ng c\u00f2n ch\u1ec9 l\u00e0 cu\u1ed9c \u0111ua v\u1ec1 k\u00edch th\u01b0\u1edbc model. H\u1ea1 t\u1ea7ng AI Infrastructure \u0111ang tr\u1edf th\u00e0nh y\u1ebfu t\u1ed1 quy\u1ebft \u0111\u1ecbnh kh\u1ea3 n\u0103ng m\u1edf r\u1ed9ng c\u1ee7a doanh nghi\u1ec7p.<\/p>\n
Khi AI workload ti\u1ebfp t\u1ee5c t\u0103ng nhanh, c\u00e1c c\u00f4ng ngh\u1ec7 t\u1ed1i \u01b0u compute nh\u01b0 Structural Sparsity<\/strong> s\u1ebd ng\u00e0y c\u00e0ng quan tr\u1ecdng. Nhi\u1ec1u chuy\u00ean gia d\u1ef1 \u0111o\u00e1n t\u01b0\u01a1ng lai AI s\u1ebd t\u1eadp trung m\u1ea1nh v\u00e0o efficiency, performance\/watt v\u00e0 low-latency inference thay v\u00ec ch\u1ec9 m\u1edf r\u1ed9ng s\u1ed1 l\u01b0\u1ee3ng parameter.<\/p>\n
\u0110i\u1ec1u n\u00e0y \u0111\u1eb7c bi\u1ec7t quan tr\u1ecdng v\u1edbi doanh nghi\u1ec7p tri\u1ec3n khai AI chatbot, AI vision, AI automation hay private LLM v\u00ec chi ph\u00ed GPU hi\u1ec7n nay v\u1eabn r\u1ea5t \u0111\u1eaft \u0111\u1ecf.<\/p>\n
T\u1ed5ng k\u1ebft<\/h2>\n
Structural Sparsity kh\u00f4ng ch\u1ec9 l\u00e0 k\u1ef9 thu\u1eadt gi\u1ea3m parameter, m\u00e0 \u0111ang tr\u1edf th\u00e0nh m\u1ed9t trong nh\u1eefng c\u00f4ng ngh\u1ec7 n\u1ec1n t\u1ea3ng gi\u00fap AI hi\u1ec7n \u0111\u1ea1i v\u1eadn h\u00e0nh hi\u1ec7u qu\u1ea3 h\u01a1n tr\u00ean quy m\u00f4 l\u1edbn. B\u1eb1ng c\u00e1ch lo\u1ea1i b\u1ecf c\u00e1c ph\u00e9p t\u00ednh kh\u00f4ng c\u1ea7n thi\u1ebft, GPU c\u00f3 th\u1ec3 t\u0103ng t\u1ed1c inference, gi\u1ea3m \u0111i\u1ec7n n\u0103ng ti\u00eau th\u1ee5 v\u00e0 c\u1ea3i thi\u1ec7n hi\u1ec7u su\u1ea5t x\u1eed l\u00fd cho c\u00e1c m\u00f4 h\u00ecnh AI ng\u00e0y c\u00e0ng ph\u1ee9c t\u1ea1p.<\/p>\n
V\u1edbi d\u1ecbch v\u1ee5 VNSO Cloud GPU<\/span> ch\u1ec9 t\u1eeb 59K\/gi\u1edd<\/a>, doanh nghi\u1ec7p v\u00e0 developer c\u00f3 th\u1ec3 d\u1ec5 d\u00e0ng s\u1eed d\u1ee5ng GPU AI hi\u1ec7u n\u0103ng cao nh\u01b0 NVIDIA A100 hay NVIDIA H100 \u0111\u1ec3 x\u00e2y d\u1ef1ng, fine-tune v\u00e0 t\u0103ng t\u1ed1c AI workload v\u1edbi chi ph\u00ed t\u1ed1i \u01b0u h\u01a1n.<\/p>\n
>>> Li\u00ean h\u1ec7 VNSO v\u00e0 thu\u00ea ngay Cloud GPU NVIDIA A100, tri\u1ec3n khai t\u1ef1 \u0111\u1ed9ng trong 5 ph\u00fat:<\/strong><\/p>\n
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