Mermaid diagram samples
This page exists for one reason: to check that the blog theme styles every Mermaid diagram type the bundled renderer (v11.16) can draw — not just flowcharts. If a diagram below looks unstyled (default purple, white background panel, invisible axis lines), it's a gap in blog_share_theme.css, and this page is where it shows up. All sources are deliberately minimal so the styling, not the content, is the test.
Flowchart#
flowchart LR
A[Your prompt] --> B{Fits in cache?}
B -- yes --> C[Reuse KV]
B -- no --> D[Prefill everything]
C --> E[Decode]
D --> ESequence#
sequenceDiagram
autonumber
actor User as Client
participant S as llama-server
participant M as Model
Client->>S: chat/completions
activate S
S->>M: forward pass
M-->>S: next token
loop until EOT
S->>M: decode step
M-->>S: token
end
S-->>Client: stream SSE
deactivate S
Note over Client,S: timings in every responseClass#
classDiagram
class TokenBucket {
+int capacity
+refill() void
}
class RateLimiter {
+check() bool
}
RateLimiter --> TokenBucket : uses
TokenBucket <|-- BurstBucket : extendsState#
stateDiagram-v2
[*] --> Disarmed
Disarmed --> Armed : operator
Armed --> EmergencyStop : watchdog
EmergencyStop --> Disarmed : reset
state pick <<choice>>
Disarmed --> pick
pick --> Armed : okER#
erDiagram
EXPOSURE ||--o{ STACK : contains
EXPOSURE {
int id
string filter
}
STACK {
int id
date night
}Gantt#
gantt
title Bench plan
dateFormat YYYY-MM-DD
section Setup
RAM upgrade :done, a1, 2026-09-10, 1d
section Runs
Fit A/B sweeps :active, b1, 2026-09-12, 1d
Spec A/B :crit, b2, after b1, 1d
Production cut :milestone, b3, after b2, 0dUser journey#
journey
title Serving a 95 GB model
Order RAM: 5: Me
Wait for delivery: 2: Me
First token arrives: 4: Me, GPU
Parity sync ruins benchmark: 1: ArrayPie#
pie title Where a decode token spends its time
"expert reads (RAM)" : 62
"attention + KV (VRAM)" : 21
"n-gram lookup (SSD)" : 12
"sampling" : 5Quadrant#
quadrantChart
title Homelab GPUs
x-axis Low VRAM --> High VRAM
y-axis Slow RAM --> Fast RAM
quadrant-1 Dream machine
quadrant-2 Tolerated
quadrant-3 The grind
quadrant-4 Sweet spot
RTX 3060 + DDR4: [0.25, 0.30]
RTX 4090 + DDR5: [0.90, 0.85]
Jetson: [0.55, 0.20]XY chart (bars + line)#
xychart-beta
title "Decode t/s across the context ladder"
x-axis [220, "1.9k", "7.5k", "22.3k", "59.6k"]
y-axis "tokens/s" 6 --> 10
bar [8.9, 9.2, 8.6, 8.1, 6.8]
line [8.8, 9.3, 8.8, 8.1, 6.7]Mindmap#
mindmap
root((Flash-Next))
RAM
routed experts
never pageable
SSD
n-gram table
2.7 KB per token
VRAM
attention
KV cacheTimeline#
timeline
title The ten days
2026-09-02 : 35B article live
2026-09-11 : 94.5 GB download done : thrashing at 1,2 t/s
2026-09-12 : 96 GB installed : 9,2 t/s measuredGit graph#
gitGraph
commit
branch develop
checkout develop
commit
commit
checkout main
merge develop
commit tag: "v1"Packet#
packet-beta
0-31: "Magic"
32-47: "Length"
48-55: "Type"
56-63: "Flags"
64-95: "Payload"Architecture#
architecture-beta
group rack(cloud)[Home rack]
service srv(server)[Unraid] in rack
service gpu(server)[RTX 3060] in rack
service db(database)[GGUF store] in rack
service net(internet)[Clients]
srv:L--R:gpu
db:T--B:srv
srv:R-->L:netSankey#
%%{init: {"sankey": {"nodeColors": {"Prompt tokens": "#83a598", "Prefill": "#fabd2f", "KV cache": "#fb4934", "Decode step": "#b8bb26", "Expert reads": "#d3869b", "Attention": "#fe8019", "RAM": "#8ec07c", "VRAM": "#a89984"}}}%%
sankey-beta
Prompt tokens,Prefill,90
Prefill,KV cache,90
Decode step,Expert reads,80
Decode step,Attention,20
Expert reads,RAM,80
Attention,VRAM,20Block#
block-beta
columns 2
a["Request"] space b["llama-server"]
b --> c["VRAM"]
b --> d["RAM"]Requirement#
requirementDiagram
requirement failsafe {
id: "R-1"
text: "Drive starts disarmed"
risk: high
verifymethod: test
}
element vcu {
type: "component"
}
vcu - satisfies -> failsafeKanban#
kanban
Todo[not planned]
[VRAM check ctx 98k]
Doing[in progress]
[Spec A/B runs]
Done[shipped]
[96 GB install]C4#
C4Context
title Serving stack
Person(op, "Operator", "me")
System_Boundary(homelab, "Rack") {
System(server, "llama-server", "12 GB GPU")
SystemDb(model, "GGUF store", "95 GB mmap")
}
Rel(op, server, "prompts", "HTTPS")
Rel(server, model, "reads")Venn#
venn-beta
set gpu ["GPU-bound"]:20
set ram ["RAM-bound"]:25
union gpu, ram:12Info#
info
title Mermaid v11.16